{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer Filter out Overdue Reviews\n",
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/candidate/filter-out-overdue-reviews.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\"\n",
    "\n",
    "# Set it to True if you don't want the optimizer to use the review logs from suspended cards.\n",
    "filter_out_suspended_cards = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import math\n",
    "from typing import List, Optional\n",
    "from datetime import timedelta, datetime\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import Tensor\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from itertools import accumulate\n",
    "from tqdm.auto import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deck file extracted successfully!\n",
      "revlog.csv saved.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "097e3d9f685e4557ae3fbeda7b6b5378",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9791 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b56fce1594914c02a5adba973ba8f3ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25097 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e015eaa3352439e9152f416acdf5d14",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "analysis:   0%|          | 0/481 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis saved!\n",
      "1:again, 2:hard, 3:good, 4:easy\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.1          0.892        1.0     inf       2044\n",
      "            1,3           3.1          0.920        4.0    4.00       1585\n",
      "          1,3,3           7.1          0.910        7.8    1.95       1305\n",
      "        1,3,3,3          16.6          0.863       10.7    1.37        976\n",
      "      1,3,3,3,3          36.5          0.839       22.2    2.07        657\n",
      "    1,3,3,3,3,3          77.0          0.861       36.4    1.64        358\n",
      "  1,3,3,3,3,3,3         117.6          0.906       38.3    1.05        176\n",
      "              2           1.0          0.902        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.2    7.45        201\n",
      "          2,3,3          11.4          0.890        7.6    0.93        162\n",
      "              3           1.1          0.977        5.0     inf       4651\n",
      "            3,3           3.3          0.967       11.9    2.38       4193\n",
      "          3,3,3           8.1          0.959       19.8    1.66       3740\n",
      "        3,3,3,3          18.2          0.940       41.3    2.09       2813\n",
      "      3,3,3,3,3          35.3          0.929       50.2    1.22       1750\n",
      "    3,3,3,3,3,3          65.2          0.929       98.2    1.96       1093\n",
      "  3,3,3,3,3,3,3         101.3          0.955      151.3    1.54        520\n",
      "3,3,3,3,3,3,3,3         135.8          0.990     1655.4   10.94        124\n",
      "              4           1.9          0.976       10.1     inf       2707\n",
      "            4,3           3.7          0.976       19.8    1.96       2219\n",
      "          4,3,3           8.2          0.967       31.6    1.60       1978\n",
      "        4,3,3,3          19.5          0.957       52.7    1.67       1641\n",
      "      4,3,3,3,3          41.4          0.957       99.2    1.88       1131\n",
      "    4,3,3,3,3,3          71.2          0.953      115.2    1.16        508\n",
      "  4,3,3,3,3,3,3          80.5          0.980      122.8    1.07        235\n",
      "4,3,3,3,3,3,3,3         115.4          0.983      169.8    1.38        157\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Step 1\"\"\"\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Deck file extracted successfully!\")\n",
    "\n",
    "\"\"\"Step 2\"\"\"\n",
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(f\"\"\"\n",
    "SELECT *\n",
    "FROM revlog\n",
    "WHERE cid IN (\n",
    "    SELECT id\n",
    "    FROM cards\n",
    "    {\"WHERE queue != -1\" if filter_out_suspended_cards else \"\"}\n",
    ")\n",
    "\"\"\"\n",
    ")\n",
    "revlog = res.fetchall()\n",
    "if len(revlog) == 0:\n",
    "    raise Exception(\"No review log found!\")\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl', 'last_ivl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000)].copy()\n",
    "\n",
    "df_set_due_date = df[(df['type'] == 4) & (df['ivl'] > 0)]\n",
    "df.drop(df_set_due_date.index, inplace=True)\n",
    "\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize('UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['create_date'].dt.year < 2006].index, inplace=True)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize('UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "\n",
    "df['is_learn_start'] = (df['type'] == 0) & (df['type'].shift() != 0)\n",
    "df['sequence_group'] = df['is_learn_start'].cumsum()\n",
    "last_learn_start = df[df['is_learn_start']].groupby('cid')['sequence_group'].last()\n",
    "df['last_learn_start'] = df['cid'].map(last_learn_start).fillna(0).astype(int)\n",
    "df['mask'] = df['last_learn_start'] <= df['sequence_group']\n",
    "df = df[df['mask'] == True].copy()\n",
    "df.drop(columns=['is_learn_start', 'sequence_group', 'last_learn_start', 'mask'], inplace=True)\n",
    "df = df[(df['type'] != 4)].copy()\n",
    "\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=int(next_day_starts_at))\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = ((df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)).astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[int(i)] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df.groupby('cid').filter(lambda group: group['id'].min() > time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000)\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "\n",
    "\n",
    "df['last_ivl'] = df['last_ivl'].map(lambda x: max(1, round(-x/86400)) if x < 0 else x)\n",
    "df['overdue_rate'] = df['delta_t'] / df['last_ivl']\n",
    "df = df[df['overdue_rate'] < 4]\n",
    "del df['overdue_rate']\n",
    "\n",
    "def remove_non_continuous_rows(group):\n",
    "    discontinuity = group['i'].diff().fillna(1).ne(1)\n",
    "    if not discontinuity.any():\n",
    "        return group\n",
    "    else:\n",
    "        first_non_continuous_index = discontinuity.idxmax()\n",
    "        return group.loc[:first_non_continuous_index-1]\n",
    "\n",
    "df = df.groupby('cid', as_index=False, group_keys=False).progress_apply(remove_non_continuous_rows)\n",
    "\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_ivl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in tqdm(df.index, desc=\"analysis\"):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)].copy()\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"Analysis saved!\")\n",
    "    caption = \"1:again, 2:hard, 3:good, 4:easy\\n\"\n",
    "    analysis = df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False)\n",
    "    print(caption + analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2, -0.2, 0.2, 1]\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w: List[float]):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w, dtype=torch.float32))\n",
    "\n",
    "    def stability_after_success(self, state: Tensor, new_d: Tensor, r: Tensor) -> Tensor:\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "\n",
    "    def stability_after_failure(self, state: Tensor, new_d: Tensor, r: Tensor) -> Tensor:\n",
    "        new_s = self.w[9] * torch.pow(new_d, self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X: Tensor, state: Tensor) -> Tensor:\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] + self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] + self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs: Tensor, state: Optional[Tensor]=None) -> Tensor:\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init: Tensor, current: Tensor) -> Tensor:\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "class WeightClipper:\n",
    "    def __init__(self, frequency: int=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(-5, -0.1)\n",
    "            w[4] = w[4].clamp(-5, -0.1)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(-0.8, -0.15)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(-2, -0.01)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line: str) -> Tensor:\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe: pd.DataFrame):\n",
    "        if dataframe.empty:\n",
    "            raise ValueError('Training data is inadequate.')\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "\n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source: RevlogDataset, batch_size: int):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            if remainder == 0:\n",
    "                self.batch_indices = np.split(indices, full_batches)\n",
    "            else:\n",
    "                self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder > 0:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from (self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist())\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Trainer:\n",
    "    def __init__(self, train_set: pd.DataFrame, test_set: pd.DataFrame, init_w: List[float], n_epoch: int=1, lr: float=1e-2, batch_size: int=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set: pd.DataFrame, test_set: pd.DataFrame):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self, verbose: bool=True):\n",
    "        # pretrain\n",
    "        best_loss = np.inf\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        pbar = tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader) * self.n_epoch)\n",
    "        for k in range(self.n_epoch):\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            tqdm.write(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "        for k in range(self.n_epoch):\n",
    "            weighted_loss, w = self.eval()\n",
    "            if weighted_loss < best_loss:\n",
    "                best_loss = weighted_loss\n",
    "                best_w = w\n",
    "\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if verbose and (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    tqdm.write(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        tqdm.write(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        return best_w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            tran_loss = self.loss_fn(retentions, labels)/len(self.train_set)\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            tqdm.write(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "\n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            test_loss = self.loss_fn(retentions, labels)/len(self.test_set)\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            tqdm.write(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        fig = plt.figure()\n",
    "        ax = fig.gca()\n",
    "        ax.plot(self.avg_train_losses, label='train')\n",
    "        ax.plot(self.avg_eval_losses, label='test')\n",
    "        ax.set_xlabel('epoch')\n",
    "        ax.set_ylabel('loss')\n",
    "        ax.legend()\n",
    "        return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb110e7fda5a4fd6910d5ea732a191bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/86046 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 55950 TEST: 30096\n",
      "dataset built\n",
      "Loss in trainset: 0.3310\n",
      "Loss in testset: 0.3142\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3e41ea21dfe4183ae26d65e736b0983",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/14973 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.2228, 1.8934, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9df1f0afc1f34a96b995514ebbb0e797",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/152877 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3286\n",
      "Loss in testset: 0.3117\n",
      "iteration: 15360\n",
      "w: [1.1823, 2.0843, 5.2864, -1.3987, -1.2774, 0.05, 1.438, -0.15, 0.8479, 2.0454, -0.1721, 0.2733, 0.9512]\n",
      "iteration: 30720\n",
      "w: [1.1801, 2.0946, 5.2254, -1.5936, -1.4485, 0.05, 1.4211, -0.2397, 0.8337, 1.9981, -0.2334, 0.3685, 0.8674]\n",
      "iteration: 46080\n",
      "w: [1.18, 2.095, 5.1134, -1.7127, -1.5431, 0.05, 1.4973, -0.1796, 0.8986, 1.9864, -0.2526, 0.3336, 0.681]\n",
      "Loss in trainset: 0.3158\n",
      "Loss in testset: 0.2977\n",
      "iteration: 61199\n",
      "w: [1.18, 2.0951, 5.0015, -1.6486, -1.6568, 0.05, 1.507, -0.2475, 0.8985, 2.0468, -0.1941, 0.3659, 0.6464]\n",
      "iteration: 76559\n",
      "w: [1.18, 2.0951, 4.923, -1.6699, -1.7041, 0.05, 1.5263, -0.196, 0.9097, 1.9629, -0.2794, 0.2767, 0.4783]\n",
      "iteration: 91919\n",
      "w: [1.18, 2.0951, 4.8915, -1.7542, -1.6841, 0.05, 1.476, -0.2567, 0.8552, 2.0611, -0.1853, 0.3837, 0.5584]\n",
      "Loss in trainset: 0.3151\n",
      "Loss in testset: 0.2974\n",
      "iteration: 107038\n",
      "w: [1.18, 2.0951, 4.8448, -1.8273, -1.6958, 0.05, 1.5357, -0.188, 0.9125, 2.0224, -0.2282, 0.3467, 0.4593]\n",
      "iteration: 122398\n",
      "w: [1.18, 2.0951, 4.8735, -1.8855, -1.717, 0.05, 1.5004, -0.2261, 0.8767, 2.0307, -0.2204, 0.3518, 0.4502]\n",
      "iteration: 137758\n",
      "w: [1.18, 2.0951, 4.8556, -1.8741, -1.726, 0.05, 1.5152, -0.2132, 0.8902, 2.0248, -0.2267, 0.3522, 0.4405]\n",
      "iteration: 153118\n",
      "w: [1.18, 2.0951, 4.8554, -1.8749, -1.7276, 0.05, 1.515, -0.2127, 0.8899, 2.0231, -0.2284, 0.3502, 0.4378]\n",
      "Loss in trainset: 0.3148\n",
      "Loss in testset: 0.2973\n",
      "TRAIN: 58274 TEST: 27772\n",
      "dataset built\n",
      "Loss in trainset: 0.3196\n",
      "Loss in testset: 0.3367\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a26381cee3d14a9899fb8e1e5280d00e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/22074 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.969, 2.0038, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a3135c0c66d48df9c705678a46c3d21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/152748 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3167\n",
      "Loss in testset: 0.3351\n",
      "iteration: 15360\n",
      "w: [2.0563, 2.1027, 5.3714, -1.672, -1.2909, 0.05, 1.4451, -0.162, 0.8379, 1.9601, -0.2492, 0.3836, 1.0399]\n",
      "iteration: 30720\n",
      "w: [2.0607, 2.1077, 5.1536, -1.6821, -1.4466, 0.05, 1.4714, -0.184, 0.8743, 1.9748, -0.2491, 0.3605, 0.8522]\n",
      "iteration: 46080\n",
      "w: [2.0609, 2.1079, 5.1959, -1.8971, -1.7918, 0.05, 1.4583, -0.1843, 0.8452, 1.9679, -0.2563, 0.3325, 0.7652]\n",
      "Loss in trainset: 0.3036\n",
      "Loss in testset: 0.3225\n",
      "iteration: 61156\n",
      "w: [2.0609, 2.108, 5.0865, -1.9104, -1.988, 0.05, 1.5028, -0.2347, 0.8791, 1.9281, -0.3014, 0.329, 0.7488]\n",
      "iteration: 76516\n",
      "w: [2.0609, 2.108, 4.8886, -1.9013, -1.916, 0.05, 1.5034, -0.2179, 0.8731, 1.9329, -0.2976, 0.3022, 0.7197]\n",
      "iteration: 91876\n",
      "w: [2.0609, 2.108, 4.8432, -1.9795, -1.9337, 0.05, 1.4938, -0.1931, 0.8659, 1.9805, -0.2544, 0.3257, 0.7472]\n",
      "Loss in trainset: 0.3025\n",
      "Loss in testset: 0.3218\n",
      "iteration: 106952\n",
      "w: [2.0609, 2.108, 4.847, -1.9756, -2.0045, 0.05, 1.4596, -0.2277, 0.8255, 2.0031, -0.2332, 0.3495, 0.6892]\n",
      "iteration: 122312\n",
      "w: [2.0609, 2.108, 4.8195, -2.0014, -2.0119, 0.05, 1.4726, -0.2089, 0.8353, 2.0143, -0.2223, 0.3596, 0.6684]\n",
      "iteration: 137672\n",
      "w: [2.0609, 2.108, 4.7989, -1.9896, -2.0271, 0.0503, 1.4943, -0.1873, 0.8561, 1.9981, -0.2389, 0.3475, 0.6546]\n",
      "iteration: 153032\n",
      "w: [2.0609, 2.108, 4.7978, -1.9896, -2.027, 0.05, 1.4939, -0.1871, 0.8555, 1.9976, -0.2393, 0.3468, 0.6535]\n",
      "Loss in trainset: 0.3024\n",
      "Loss in testset: 0.3217\n",
      "TRAIN: 57868 TEST: 28178\n",
      "dataset built\n",
      "Loss in trainset: 0.3250\n",
      "Loss in testset: 0.3255\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c454e3034e3b4febb263351335ad3e23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/20805 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.239, 1.8371, 5.0, -0.5, -0.5, 0.2, 1.4, -0.2, 0.8, 2.0, -0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6192302a0924d4180bfeedbf1bdca16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/152799 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3227\n",
      "Loss in testset: 0.3229\n",
      "iteration: 15360\n",
      "w: [1.1681, 1.9247, 5.3234, -1.4693, -1.0827, 0.05, 1.4209, -0.234, 0.8183, 2.0206, -0.175, 0.2511, 0.963]\n",
      "iteration: 30720\n",
      "w: [1.1644, 1.9292, 5.2393, -1.7555, -1.31, 0.05, 1.4937, -0.2416, 0.8978, 1.9319, -0.3068, 0.1931, 0.9789]\n",
      "iteration: 46080\n",
      "w: [1.1643, 1.9295, 5.1588, -1.8851, -1.4618, 0.05, 1.5269, -0.15, 0.9247, 1.9861, -0.2902, 0.3548, 1.0169]\n",
      "Loss in trainset: 0.3090\n",
      "Loss in testset: 0.3095\n",
      "iteration: 61173\n",
      "w: [1.1643, 1.9295, 5.109, -1.7257, -1.6314, 0.05, 1.4757, -0.2687, 0.8698, 2.0069, -0.2795, 0.3192, 0.9103]\n",
      "iteration: 76533\n",
      "w: [1.1643, 1.9295, 5.0307, -1.8659, -1.6702, 0.05, 1.5394, -0.2111, 0.9281, 2.0008, -0.2951, 0.3422, 0.8869]\n",
      "iteration: 91893\n",
      "w: [1.1643, 1.9295, 4.9728, -1.8177, -1.6961, 0.05, 1.4973, -0.2333, 0.8812, 1.9761, -0.3243, 0.3359, 0.8458]\n",
      "Loss in trainset: 0.3090\n",
      "Loss in testset: 0.3094\n",
      "iteration: 106986\n",
      "w: [1.1643, 1.9295, 4.9235, -1.8481, -1.7435, 0.05, 1.558, -0.1783, 0.9333, 2.0343, -0.2679, 0.3692, 0.8403]\n",
      "iteration: 122346\n",
      "w: [1.1643, 1.9295, 4.9634, -1.9051, -1.7791, 0.05, 1.5102, -0.2306, 0.885, 2.026, -0.2758, 0.3487, 0.8195]\n",
      "iteration: 137706\n",
      "w: [1.1643, 1.9295, 4.9549, -1.9159, -1.7857, 0.05, 1.5229, -0.2164, 0.8964, 2.036, -0.2663, 0.3576, 0.8184]\n",
      "iteration: 153066\n",
      "w: [1.1643, 1.9295, 4.9516, -1.9141, -1.7856, 0.05, 1.5248, -0.2149, 0.8981, 2.0345, -0.268, 0.3564, 0.8164]\n",
      "Loss in trainset: 0.3087\n",
      "Loss in testset: 0.3092\n",
      "\n",
      "Training finished!\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nsVgsWCwW9uzZw+bNm7n22msJCgoiMjKSW265hSNHjrie995779G7d2/8/f1p2bIlw4cPp6ioiIceeojXXnuN//73v67zrV69+oKPS5fzGpGnXM47l4JTZXywYT9vrsthe/4JV3v3NiFMTIxhVFxbgv18zn8ihwO+/9y5bmrP/860xwxxrpvqeq3WTYmInEe1l6AMA8qKL3wxPgG1vll9QUEB1157Lb169eLhhx92Pt3Hh+7du3PHHXcwZcoUTp06xfTp0ykvL+eLL77g4MGDxMTE8Nhjj3HTTTdx4sQJ/ve//zFlyhQAbr/9dgoLC3n11VeBM3tB1laT2CdK3Fuovw+3DunIlMEdyMo5xhtrc/h400G2HizkwQ82k/LpVn4V15aJCR3o3f4c99ezWqHrNc7HwW8h7d+w+T3I+cb5aNHJuW4qbiL4Bl24AYqIeLqyYnik7YV/3b8eAFtgrbqGhoZis9kICAhw3Trt//2//0e/fv145JFHXP1eeeUVoqOj2bFjBydPnqS8vJxf//rXdOjQAYDevXu7+vr7+1NSUnLO28Q1Nl3Ok1qxWCwM6BDOU+PiSP/rVTx4fQ+6tAqkuNTOW+m53PDcV9ww9yveSs+hqGLrhBq16Qu/fgH+uAkuTQa/MDi2Gz77MzzdA1bMhoL9F2RcIiJijm+//ZZVq1YRFBTkenTr1g2AH374gb59+3LVVVfRu3dvbr75Zl588UWOHTt2nrNeWLqc14iawuW8czEMg/TdR3ljXQ7LNudRancAEOTrzah+ztmpHm1rMe7SIsh+07kb+tFdzjarN/Qa7ZydahvXeIMQEfEgnno5D87+dN61115LQEAAjz766Fl927RpQ2BgIIZh8M033/D555/z/vvvk5eXx7p16+jUqZNbfDpPl/Ok3iwWC4mdW5LYuSVHi0p5LzOXt9Jz2X2kiEVrc1i0Noe46DAmJsZwQ5+2+NtqWPNkC4SEOyH+dtixzLl5596vnBt5blwMHS51rpu6+BrnZUERETnDYqn1ZTUz2Ww27Ha76+v+/fuzZMkSOnbsiLd39XHEYrFwySWXcMkllzBr1iw6dOjA+++/T3Jy8lnnM4N+I0mDCA+0cdfQLqQmX86bdySS1LsN3lYL2bnH+ct7G0l4ZCWz/7uZ7Xknaj6J1QrdroOpn8Bdq6H3WOeM1N6v4O0J8Fw8pL/onLkSERGP0rFjR9atW8eePXs4cuQI06ZN4+jRo0yYMIGMjAx++OEHli9fztSpU7Hb7axbt45HHnmE9evXk5OTw9KlSzl8+DDdu3d3nW/jxo1s376dI0eOUFZWi214Gpgu5zWipn4573wOnyjh3cxc3krPIffoKVd7fIcWTEyM4brebfDzOc8n8gr2Q/p/IPNVOF2xs7p/C4j/DQy8E0LaNOIIRETciydvtrljxw5uvfVWvv32W06dOsXu3bspKytj+vTprFq1ipKSEjp06MA111zDU089xbZt27jvvvvIysqisLCQDh06cO+993LPPfcAcPjwYSZNmkRaWhonT55k1apVDBs2rNb1aMdyN9fcQ1Qlh8Pgq51HeGPdXlZuPYTd4fxPLtTfh9H92zMxMYbY1uf5RF7JyTPrpo7tdrZZfaD3GOe6qTZ9GnkUIiLm8+QQ5W4UotycQtTZ8gtP805GLm9n5LL/+JnZqcRO4UxMjOGaXlH4ep9jdsphh+2fOddN5Xxzpr3TUOdNj2Ov1ropEWmyFKIajkKUm1OIqpndYfDljsO8sS6HL7blUzE5RXigjTED2jMhIYZOEedZKLk/07nf1Jb3wahYXNjyIhh8N/QZD7aAxh2EiMgFphDVcBSi3JxCVO0cOH6KxRm5LM7IJa/wtKv9ktiWTEzowNU9IrF5n2N26Xhuxbqp15y3lgHnPfsG3u5cNxUc2cgjEBG5MBSiGo5ClJtTiKqbcruDVdsP8+a6vazecZjK/zIjgmzcHB/NhIExxLQ8x+xSyQnY8IZz3dTxvc42Lxv0vtm5biqqV+MPQkSkESlENRyFKDenEFV/uUeLnbNT63M5fKIEcG6FctlFrZiYEMNV3Vvj41XD7JTDDts+ca6byl17pr3zMOe6qS5Xad2UiHikyl/8HTt2xN/f3+xyPNqpU6fYs2ePQpS7Uoj65crsDlK35vPGuhz+9/2ZO3u3DvZl3MBoxifE0C7sHP8j2bfeedPj7/4LhnNHdSK6VqybGgc++p+QiHgOu93Ojh07aN26NS1btjS7HI/2448/cujQIS6++GK8vKp+oEkhyg0oRDWsvT8W8VZ6Lu+uz+XHolLAOTt1RdfWTEyIYVjXVnjXNDt1bO+ZdVOlFRt+BrSEgXc4H0GtL9AoRER+mYMHD3L8+HFat25NQEAAljrcekWctywrLi7m0KFDhIWF0abN2fsNKkS5AYWoxlFa7uDz7/J4c10O3/zwo6u9Tagf4wZGM25gNG1Ca5hhOl0IGxbC2uehIMfZ5mWDPmNh0DSI7HEBRiAiUn+GYZCXl8fx48fNLsWjhYWFERUVVW0IVYhyAwpRjW/X4ZO8lZ7De5n7OFbs3PLfaoEru0UyaVAMQy9qhZe1mn+l2cth20fwzXOwf/2Z9i5XOu/T1+WqOt1YU0TkQrPb7abc6qQp8PHxOesS3k8pRLkBhagL53SZneVb8nhjXQ7pu4+62tuF+TMhIZqx8dG0Dqnhkyy56c51U1s/OrNuqlV3Z5jqfTP46BMwIiLNiUKUG1CIMsf3+Sd4Mz2HJZn7KDxdDoC31cLw7s7ZqUu6RGCtbnbq2B5Y9wJkvQ6lJ51tga2ca6bib4egVhduECIiYhqFKDegEGWu02V2Ptl4kDfTc8jce8zVHhMewISEGG6Ob09EkG81TyxwBqm1z0PhPmebly/0He/cb6p1tws0AhERMYNClBtQiHIf2/IKeWtdDkuz9nOixDk75eNlYUTPKCYlxjC4c8uzFxfay2Hrf53rpg5knWmPvdp5qa/zMK2bEhFpghSi3IBClPspLi3n428P8kZ6Dt/mHne1d44IZEJCDKMHtCc80Fb1SYYBuesq1k19DFT8yLTuWbFuagx4VzOjJSIiHkkhyg0oRLm3LQcKeHNdDh9s2E9RqfMGxjYvK9f2jmJSYgcGdmxx9uzU0V0V66YWQlmRsy2wNSTcBfG/gUBtfici4ukUotyAQpRnOFlSzofZB3gzfS+b9xe62mNbBzExIYbR/dsTGuBT9UmnjkPWa85AVbjf2ebtB30nONdNtbr4wg1AREQalEKUG1CI8jwb9x3njbU5fPjtAU6VOWenfL2tJPVpw6TEGPrH/Gx2yl7mvKXMN3PhYPaZ9otGOi/1dRqqdVMiIh5GIcoNKER5rsLTZfx3w37eWJfDtrwTrvZuUcFMTIxhVL92hPj9ZHbKMCAnzXnT422f4Fo3FdnbGaZ6jQbvn621EhERt6QQ5QYUojyfYRhsyHXOTn288QAl5c7NOP19vLihbxsmJnagb/vQqrNTP/4A656HDYugrNjZFhQFCXc6100FhJswEhERqS2FKDegENW0FBSXsXTDPt5cl8P3h0662nu0CWHSoBh+FdeOIF/vM08oPgqZC5w3Pj5x0Nnm7Q9xE53rpiJiL+wARESkVhSi3IBCVNNkGAbr9x7jjbV7+XRzHqUVs1OBNi9ujGvHpMQYerULPfOE8lLY8r5zi4S8jRWNFrh8Olwx88IPQEREzkkhyg0oRDV9x4pKWZLlnJ3adaTI1d63fSgTE2O4oW9bAmwVs1OGAXu+cq6b2vEZWL3hj5shpI1J1YuISHUUotyAQlTzYRgGa3cd5c30HJZtPkiZ3fljFezrzah+7ZiYGEP3Nj/5b+CVa5wL0S+fodkoERE3oxDlBhSimqcjJ0t4L3Mfb6XnsPfHYld7/5gwJiZ24Po+bfDb9j4sud254Py+zeDlc44ziojIhaQQ5QYUopo3h8Pgmx9+5M30vXy+JZ9yh/NHLcTPm5vjIvnrjjF4FR+GMa9Cr1+bXK2IiFSq7e9v6wWsSaRZsVotXHpRBP+eNIBvZl7Jn0d2pX0LfwpPl/Py2v28Zb/S2THjZXMLFRGRenGLEDVv3jw6duyIn58fiYmJpKen19h36dKlxMfHExYWRmBgIHFxcSxcuNB1vKysjOnTp9O7d28CAwNp27YtU6ZM4cCBA64+e/bs4fbbb6dTp074+/vTpUsXZs+eTWlpaZU+FovlrMfatWsb55sgTVrrYD+mXRHLl3++ggVTBxLk683cgsswLF6w9yvI/87sEkVEpI5MD1GLFy8mOTmZ2bNnk5WVRd++fRk5ciSHDh2qtn94eDgPPPAAaWlpbNy4kalTpzJ16lSWL18OQHFxMVlZWTz44INkZWWxdOlStm/fzo033ug6x7Zt23A4HLzwwgts2bKFp59+mueff56//vWvZ73eypUrOXjwoOsxYMCAxvlGSLNgtVoY1rU1N/VrRz7hbAi4xHkg4yVzCxMRkTozfU1UYmIiAwcO5LnnngPA4XAQHR3Nvffey4wZM2p1jv79+5OUlMScOXOqPZ6RkUFCQgJ79+4lJiam2j6PP/448+fPZ9euXYBzJqpTp05s2LCBuLi4ug8MrYmSmm3PO8HIZ77kEq/veMPn/4EtCJK3gp/+OxERMZtHrIkqLS0lMzOT4cOHu9qsVivDhw8nLS3tvM83DIPU1FS2b9/O0KFDa+xXUFCAxWIhLCzsnH3Cw8++HceNN95I69atufTSS/nwww/PWU9JSQmFhYVVHiLV6RoVzMCOLfja3p0fAzpB6Un49m2zyxIRkTowNUQdOXIEu91OZGRklfbIyEjy8vJqfF5BQQFBQUHYbDaSkpKYO3cuV199dbV9T58+zfTp05kwYUKNaXLnzp3MnTuX3/72t662oKAgnnzySd59910++eQTLr30UkaNGnXOIJWSkkJoaKjrER0dfa7hSzM3eVAHwMIrJRX/iMh4ybkhp4iIeATv83dxP8HBwWRnZ3Py5ElSU1NJTk6mc+fODBs2rEq/srIyxo4di2EYzJ8/v9pz7d+/n2uuuYabb76ZO++809UeERFBcnKy6+uBAwdy4MABHn/88Srrq35q5syZVZ5TWFioICU1uqZXFC0DbbxWlMh9QW/gfWQ77P4SOl9udmkiIlILps5ERURE4OXlRX5+fpX2/Px8oqKianye1WolNjaWuLg47r//fsaMGUNKSkqVPpUBau/evaxYsaLaWagDBw5wxRVXMGTIEP7zn/+ct97ExER27txZ43FfX19CQkKqPERq4uvtxdiB0ZwkgNW+ldsdvGhuUSIiUmumhiibzcaAAQNITU11tTkcDlJTUxk8eHCtz+NwOCgpKXF9XRmgvv/+e1auXEnLli3Pes7+/fsZNmwYAwYM4NVXX8VqPf+3Ijs7mzZtdJ8zaTgTE2KwWODRoxVr+rZ9CgX7zS1KRERqxfTLecnJydx6663Ex8eTkJDAM888Q1FREVOnTgVgypQptGvXzjXTlJKSQnx8PF26dKGkpIRPP/2UhQsXui7XlZWVMWbMGLKysvj444+x2+2u9VXh4eHYbDZXgOrQoQNPPPEEhw8fdtVTOQP22muvYbPZ6NevH+Dcn+qVV17hpZf0UXRpONHhAQy7uBWrtsPeoH50OLkBMl+FK/9mdmkiInIepoeocePGcfjwYWbNmkVeXh5xcXEsW7bMtdg8JyenyixRUVERd999N/v27cPf359u3bqxaNEixo0bBzhnmCoXf/98a4JVq1YxbNgwVqxYwc6dO9m5cyft27ev0uenOz7MmTOHvXv34u3tTbdu3Vi8eDFjxoxpjG+DNGOTB3Vg1fbDzD15BU+wATJfg6F/AW+b2aWJiMg5mL5PVFOmfaKkNuwOg6GPrSL/+Ak2hf0J/9OHYPTL0FuBXUTEDB6xT5SIgJfVwsTEGMrx5gNrxVYd6VpgLiLi7hSiRNzA2PhofLwsPHV0CIbFG3LXQt4ms8sSEZFzUIgScQOtgn25plcbDtOCjSEVn9TT/fRERNyaQpSIm5ic6Lyv4xNHL3M2bHwHTh03ryARETknhSgRN5HQKZyLI4P4X9nFHAuKhbJi+PYts8sSEZEaKESJuAmLxcKkROf99F4rr1hgnvESOBym1iUiItVTiBJxIzf1b4e/jxcvHo+n3CcIftwJu1ebXZaIiFRDIUrEjYT4+TCqX1uK8OfrwMrtDrTAXETEHSlEibgZ5yU9SDl8ibNhx2dwPNfEikREpDoKUSJuple7UPrFhLHN3pZ9YQPBcMD6V8wuS0REfkYhSsQNTa6Yjfp30RXOhqzXobzExIpEROTnFKJE3FBSnzaEBfiw+ERvTvtHQfER2PKB2WWJiMhPKESJuCE/Hy9uHtAeO158YrvG2Zih++mJiLgThSgRNzWx4pLePw8lYlh9YF8GHMg2tygREXFRiBJxU50iArnsoggOG6F816JibZTupyci4jYUokTcWOV2B08cq7gp8ab34NQxEysSEZFKClEibmx499ZEhfixqrgTBSFdofwUbHjD7LJERASFKBG35u1lZUJCDGDhLWOks1H30xMRcQsKUSJubnxCNF5WC88ejsNuC4Fju+GHL8wuS0Sk2VOIEnFzkSF+jOgRySn8WBdSORul7Q5ERMymECXiASYPci4w/3+HhjgbdiyHY3vMK0hERBSiRDzBkC4t6RwRyHelkRxoORgwdD89ERGTKUSJeACLxcLExBgA/nP6Smdj1kIoO21iVSIizZtClIiHuHlANH4+Vl7/sTslgW3h1FHYstTsskREmi2FKBEPERrgww192uLAyoqAJGdjuhaYi4iYRSFKxINULjCfc2AghpcNDmTB/kyTqxIRaZ4UokQ8SN/oMHq3CyXfHsT3EcOdjRkvm1uUiEgzpRAl4mEmD3IuMH/q+OXOhs1LoPioiRWJiDRPClEiHuaGvm0J9vNmWUF7TrToCeWnYcNCs8sSEWl2FKJEPEyAzZvR/dsDFt7zutbZmPEyOOym1iUi0twoRIl4oMoF5o/v74nDNwyO74WdK80tSkSkmVGIEvFAsa2DGNy5JcWGL1nh1zkbtd2BiMgFpRAl4qHOup/ezpVwdJeJFYmINC8KUSIeakTPSFoF+5JdFM6hyMsAQ9sdiIhcQApRIh7Kx8vK+IHRACwoq9gzasMiKC02sSoRkeZDIUrEg01IiMFqgecPdKEsOBpOH3fuGyUiIo1OIUrEg7UN8+fKbpE4sLI6+AZnY8aLYBjmFiYi0gwoRIl4uFsGOxeYP3ygP4aXLxz8VvfTExG5ABSiRDzcZbERdGgZQO7pAPZEjXQ2arsDEZFGpxAl4uGsVgsTE5z30/vXyWHOxi1LoeiIeUWJiDQDbhGi5s2bR8eOHfHz8yMxMZH09PQa+y5dupT4+HjCwsIIDAwkLi6OhQvP3DesrKyM6dOn07t3bwIDA2nbti1TpkzhwIEDVc5z9OhRJk2aREhICGFhYdx+++2cPHmySp+NGzdy2WWX4efnR3R0NI899ljDDlykgdwcH43N28r7+VEURfQFeylkvW52WSIiTZrpIWrx4sUkJycze/ZssrKy6Nu3LyNHjuTQoUPV9g8PD+eBBx4gLS2NjRs3MnXqVKZOncry5csBKC4uJisriwcffJCsrCyWLl3K9u3bufHGG6ucZ9KkSWzZsoUVK1bw8ccf8+WXX3LXXXe5jhcWFjJixAg6dOhAZmYmjz/+OA899BD/+c9/Gu+bIVJP4YE2knq3AeAj34odzNe/qvvpiYg0IothmPsxnsTERAYOHMhzzz0HgMPhIDo6mnvvvZcZM2bU6hz9+/cnKSmJOXPmVHs8IyODhIQE9u7dS0xMDFu3bqVHjx5kZGQQHx8PwLJly7juuuvYt28fbdu2Zf78+TzwwAPk5eVhs9kAmDFjBh988AHbtm2r9nVKSkooKSlxfV1YWEh0dDQFBQWEhITU+nsiUh+Ze48yen4aId7lZAf9AevpYzD+Leh2ndmliYh4lMLCQkJDQ8/7+9vUmajS0lIyMzMZPny4q81qtTJ8+HDS0tLO+3zDMEhNTWX79u0MHTq0xn4FBQVYLBbCwsIASEtLIywszBWgAIYPH47VamXdunWuPkOHDnUFKICRI0eyfft2jh07Vu3rpKSkEBoa6npER0efdwwiDaV/TAu6RQVTWO7N5tYVM68ZWmAuItJYTA1RR44cwW63ExkZWaU9MjKSvLy8Gp9XUFBAUFAQNpuNpKQk5s6dy9VXX11t39OnTzN9+nQmTJjgSpN5eXm0bt26Sj9vb2/Cw8Ndr5uXl1dtXZXHqjNz5kwKCgpcj9zc3HOMXqRhWSwW13YHKUcuwcACP3wBR3aaXJmISNNk+pqo+ggODiY7O5uMjAz+8Y9/kJyczOrVq8/qV1ZWxtixYzEMg/nz5zd6Xb6+voSEhFR5iFxIo+LaEeTrTdrRII61HeZsXK/76YmINAZTQ1RERAReXl7k5+dXac/PzycqKqrG51mtVmJjY4mLi+P+++9nzJgxpKSkVOlTGaD27t3LihUrqgSaqKiosxaul5eXc/ToUdfrRkVFVVtX5TERdxTo681N/doB8IZjhLNxwxtQWmRiVSIiTZOpIcpmszFgwABSU1NdbQ6Hg9TUVAYPHlzr8zgcjioLuisD1Pfff8/KlStp2bJllf6DBw/m+PHjZGae2dX5iy++wOFwkJiY6Orz5ZdfUlZW5uqzYsUKunbtSosWLeo8VpELZfIg5yW9Z3NiKA/tCCUFsOldc4sSEWmCTL+cl5yczIsvvshrr73G1q1b+b//+z+KioqYOnUqAFOmTGHmzJmu/ikpKaxYsYJdu3axdetWnnzySRYuXMjkyZMBZ4AaM2YM69ev54033sBut5OXl0deXh6lpaUAdO/enWuuuYY777yT9PR0vv76a+655x7Gjx9P27ZtAZg4cSI2m43bb7+dLVu2sHjxYp599lmSk5Mv8HdIpG66RgUzsGMLyh0WvmnxK2dj+ku6n56ISAPzNruAcePGcfjwYWbNmkVeXh5xcXEsW7bMtYg7JycHq/VM1isqKuLuu+9m3759+Pv7061bNxYtWsS4ceMA2L9/Px9++CEAcXFxVV5r1apVDBs2DIA33niDe+65h6uuugqr1cro0aP517/+5eobGhrK559/zrRp0xgwYAARERHMmjWryl5SIu5q8qAOZOw5xv/b35/l3n5Y8jdBbjrEJJpdmohIk2H6PlFNWW33mRBpaCXldoakfMGPRaV81W0J7fcsgd43w+iXzC5NRMTtecQ+USLSOHy9vRg70LlP2fOnrnQ2bvkATlZ/JwAREak7hSiRJmpiQgwWCyza24LTkQPAUQZZr5ldlohIk6EQJdJERYcHMOziVgAsC7je2bj+VbCXm1iViEjToRAl0oRVbnfwj90XYwS0hML9sOMzk6sSEWkaFKJEmrBhXVvTLsyfw6ctbG97k7MxXffTExFpCApRIk2Yl9XCxMQYAJ48eglYrLB7DRzeYXJlIiKeTyFKpIkbGx+Nj5eFFQd8KYy5ytmYoa0ORER+KYUokSauVbAvI3s67/f4nvUaZ+O3b0HJSROrEhHxfApRIs3ALRULzJ/Y2RZ7i85QUggbF5tclYiIZ1OIEmkGEjqFc3FkEMVlBpmtfu1szND99EREfgmFKJFmwGKxMCmxYruDg/0xfALg0Hew9xuTKxMR8VwKUSLNxE392+Hv48W3h+FQxxudjVpgLiJSbwpRIs1EiJ8Po/q1BeCV0opP6W39EE7kmViViIjnUogSaUYqL+m9sjOIsrYJ4CiHTN1PT0SkPhSiRJqRXu1CiYsOo8xusCqk4pJe5qtgLzO3MBERD6QQJdLMVG538MjuizECW8GJg7DtE5OrEhHxPApRIs1MUp82hAX4sKegnD0xY5yNWmAuIlJnClEizYyfjxc3D2gPwNzCS53309vzPzi01eTKREQ8i0KUSDM0sWKB+fu7LBR3Huls1GyUiEidKESJNEOdIgK57KIIDAP+65PkbPz2bThdaG5hIiIeRCFKpJmq3O7g8R2ROFpeBKUndT89EZE6UIgSaaaGd29NVIgfR4vL2Nz2Zmdj+ou6n56ISC0pRIk0U95eViYkxADwRF5/8AmEI9udi8xFROS8FKJEmrHxCdF4WS18mVvKsYtucjZqgbmISK0oRIk0Y5EhfozoEQnAQvsIZ+PWj6HwgIlViYh4BoUokWZucsUO5i9s9cUePQQMO2QuMLcoEREPoBAl0swN6dKSzhGBFJXa+SZ8lLMxcwGUl5pZloiI21OIEmnmLBYLExOdC8wf3ROLERQJJ/Nh20cmVyYi4t4UokSEMQPa4+ttZXP+aQ7GjnM2pmuBuYjIuShEiQhhATZu7NsWgBdOXAYWL8j5BvK3mFyZiIj7UogSEeDMAvO3ttkpueg6Z2P6iyZWJCLi3hSiRASAvtFh9G4XSqndwfKAG5yNG9+B0wXmFiYi4qYUokTEZfKgih3Mt7fCaNUdyoog+y2TqxIRcU8KUSLickPftgT7eZNz7BTfd6hYYJ7xku6nJyJSDYUoEXEJsHkzun97AJ49PABswfDj97BrtbmFiYi4IYUoEami8pLeZztOcLL7GGej7qcnInIWhSgRqSK2dTCDO7fEYcC7jHQ2bv8UCvaZW5iIiJtRiBKRs1RudzBviw+ODpeB4YD1r5pclYiIe1GIEpGzjOgZSatgX46cLGFD1GhnY9ZrUF5ibmEiIm7E9BA1b948OnbsiJ+fH4mJiaSnp9fYd+nSpcTHxxMWFkZgYCBxcXEsXLjwrD4jRoygZcuWWCwWsrOzqxzfs2cPFoul2se7777r6lfd8bfffrtBxy7irny8rIwfGA3AUzmxENwGig7Ddx+aXJmIiPswNUQtXryY5ORkZs+eTVZWFn379mXkyJEcOnSo2v7h4eE88MADpKWlsXHjRqZOncrUqVNZvny5q09RURGXXnopjz76aLXniI6O5uDBg1Uef//73wkKCuLaa6+t0vfVV1+t0m/UqFENNnYRdzchIQarBb7eXciP3SY6GzO0g7mISCWLYZi3AUxiYiIDBw7kueeeA8DhcBAdHc29997LjBkzanWO/v37k5SUxJw5c6q079mzh06dOrFhwwbi4uLOeY5+/frRv39/Xn75ZVebxWLh/fffr1NwKikpoaTkzOWOwsJCoqOjKSgoICQkpNbnEXEXd7y2npVb87lnYBB/2vJrcJTDb/8HbfqYXZqISKMpLCwkNDT0vL+/6zUT9dprr/HJJ5+4vv7LX/5CWFgYQ4YMYe/evbU6R2lpKZmZmQwfPvxMMVYrw4cPJy0t7bzPNwyD1NRUtm/fztChQ+s+iAqZmZlkZ2dz++23n3Vs2rRpREREkJCQwCuvvML58mZKSgqhoaGuR3R0dL3rEnEHldsdvLbpNOVdK24Fo9koERGgniHqkUcewd/fH4C0tDTmzZvHY489RkREBPfdd1+tznHkyBHsdjuRkZFV2iMjI8nLy6vxeQUFBQQFBWGz2UhKSmLu3LlcffXV9RkGAC+//DLdu3dnyJAhVdoffvhh3nnnHVasWMHo0aO5++67mTt37jnPNXPmTAoKClyP3Nzcetcl4g6GXtSKmPAATpwuZ03or5yNG9+FU8fMLUxExA141+dJubm5xMbGAvDBBx8wevRo7rrrLi655BKGDRvWkPWdJTg4mOzsbE6ePElqairJycl07ty5Xq976tQp3nzzTR588MGzjv20rV+/fhQVFfH444/z+9//vsbz+fr64uvrW+c6RNyV1WphUmIMKZ9t4+kdLbmydQ8sh76D7Ddh8DSzyxMRMVW9ZqKCgoL48ccfAfj8889dM0F+fn6cOnWqVueIiIjAy8uL/Pz8Ku35+flERUXVXLDVSmxsLHFxcdx///2MGTOGlJSU+gyD9957j+LiYqZMmXLevomJiezbt6/KmieR5uDm+Ghs3lY2HzjBvosmOxszXgKHw9zCRERMVq8QdfXVV3PHHXdwxx13sGPHDq677joAtmzZQseOHWt1DpvNxoABA0hNTXW1ORwOUlNTGTx4cK1rcTgc9Q42L7/8MjfeeCOtWrU6b9/s7GxatGihmSZpdsIDbST1bgPA/B/7g28IHN0Fu74wuTIREXPV63LevHnz+Nvf/kZubi5LliyhZcuWgHOR9oQJE2p9nuTkZG699Vbi4+NJSEjgmWeeoaioiKlTpwIwZcoU2rVr55ppSklJIT4+ni5dulBSUsKnn37KwoULmT9/vuucR48eJScnhwMHDgCwfft2AKKioqrMcO3cuZMvv/ySTz/99Ky6PvroI/Lz8xk0aBB+fn6sWLGCRx55hD/96U91/E6JNA2TB8Xw/ob9LNl8nNmJ4/DNfBEyXobY4ed/sohIE1WvEBUWFubaluCn/v73v9fpPOPGjePw4cPMmjWLvLw84uLiWLZsmWuxeU5ODlbrmcmyoqIi7r77bvbt24e/vz/dunVj0aJFjBs3ztXnww8/dIUwgPHjxwMwe/ZsHnroIVf7K6+8Qvv27RkxYsRZdfn4+DBv3jzuu+8+DMMgNjaWp556ijvvvLNO4xNpKvrHtKBbVDDb8k7woc913MyLsGMZHM+BsBizyxMRMUW99olatmwZQUFBXHrppYBzZurFF1+kR48ezJs3jxYtWjR4oZ6otvtMiHiCRWv38rcPNtMpIpAvWj2NZfdquPQ+GP6Q2aWJiDSoRt0n6s9//jOFhYUAbNq0ifvvv5/rrruO3bt3k5ycXL+KRcStjerXjiBfb3YfKWJb9FhnY9brUHba3MJERExSrxC1e/duevToAcCSJUu4/vrreeSRR5g3bx6fffZZgxYoIu4hyNebm/q1A2DuvosgpD0U/wjffWBuYSIiJqlXiLLZbBQXFwOwcuVK17qi8PBw1wyViDQ9kwd1AGD5tiOc6FWx3UG6djAXkeapXiHq0ksvJTk5mTlz5pCenk5SUhIAO3bsoH379g1aoIi4j65RwQzs2AK7w+Ct8mFg9YH96+HABrNLExG54OoVop577jm8vb157733mD9/Pu3aOaf4P/vsM6655poGLVBE3EvlbNQr2cU4elTcCib9JRMrEhExR70+nSe1o0/nSVNUUm5nSMoX/FhUytvXWBi0egJ4+0HyVggIN7s8EZFfrLa/v+u1TxSA3W7ngw8+YOvWrQD07NmTG2+8ES8vr/qeUkQ8gK+3FzfHR/P8mh+YtzOcQVG9IW8TbFgEl9R8b0kRkaamXpfzdu7cSffu3ZkyZQpLly5l6dKlTJ48mZ49e/LDDz80dI0i4mYmJcZgscD/dv7I4e4V955c/7LupycizUq9QtTvf/97unTpQm5uLllZWWRlZZGTk0OnTp34/e/1L1GRpi46PIBhFzvvOfny8XjwC4Vje2DnSnMLExG5gOoVotasWcNjjz1GePiZ9Q8tW7bkn//8J2vWrGmw4kTEfVUuMH8r+wjlfSY6GzO0wFxEmo96hShfX19OnDhxVvvJkyex2Wy/uCgRcX/DuramXZg/BafKWBl4g7Px+8+dM1IiIs1AvULU9ddfz1133cW6deswDAPDMFi7di2/+93vuPHGGxu6RhFxQ15WCxMTnTcffn4z0OUqwICMl02tS0TkQqlXiPrXv/5Fly5dGDx4MH5+fvj5+TFkyBBiY2N55plnGrhEEXFXY+Oj8fGykJ17nL1dJjgbNyyEslPmFiYicgHUa4uDsLAw/vvf/7Jz507XFgfdu3cnNja2QYsTEffWKtiXkT2j+HjjQV44GMsjoTFQkAObl0K/SWaXJyLSqGq92WZycnKtT/rUU0/Vu6CmRJttSnOwbtePjPvPWvx9vNhw5Wb81syBtv3grtVmlyYiUi8Nvtnmhg21uzeWxWKp7SlFpAlI6BTOxZFB7Mg/yftcwQSvR5330tuXCe0HmF2eiEijqXWIWrVqVWPWISIeymKxMCmxA7M/3MLLG04yvudNWDYuhowXFaJEpEmr18JyEZGfuql/O/x9vNh56CRb2o1zNm5eCkU/mluYiEgjUogSkV8sxM+HUf3aAvD8zjBoEwf2Etjwuql1iYg0JoUoEWkQkxKdO5gv/y6fwj5TnY0Zr4DDbmJVIiKNRyFKRBpEr3ahxEWHUWY3eLMoHvxbOLc7+P5zs0sTEWkUClEi0mBuqbif3sL1h3DETXY26n56ItJEKUSJSINJ6tOGsAAf9h8/RVqLXwEW2LkSfvzB7NJERBqcQpSINBg/Hy9uHtAegBe3GBA73Hlg/SsmViUi0jgUokSkQU2sWGC+ZsdhDnef4mzcsBBKi02sSkSk4SlEiUiD6hQRyGUXRWAY8Ep+FwjrAKcLYPN7ZpcmItKgFKJEpMFVbnewOPMA5QMqtjtIfxFqd6tOERGPoBAlIg1uePfWRIX4cbSolM99R4C3H+RthH0ZZpcmItJgFKJEpMF5e1mZkBADwCuZBdBrtPNA+osmViUi0rAUokSkUYxPiMbLamH93mPs6TzB2fjdB3DysKl1iYg0FIUoEWkUkSF+jOgRCcBLu8Kg3QCwl0LWa+YWJiLSQBSiRKTRTK7Ywfz9rP2c7vcbZ+P6V8FebmJVIiINQyFKRBrNkC4t6RwRSFGpnaUlCRDQEgr3wY5lZpcmIvKLKUSJSKOxWCxMTHQuMH89Iw+j3y3OAxlaYC4ink8hSkQa1ZgB7fH1trIt7wSb24wGLLBrNRz53uzSRER+EYUoEWlUYQE2buzbFoCXN9vh4pHOAxkvm1iViMgvpxAlIo2ucoH5p5vyKOx9m7Mx+00oLTKvKBGRX0ghSkQaXd/oMHq3C6XU7uDNH2MhvDOUFMDGd8wuTUSk3hSiROSCmDzIucD8zfR9OAZUbHeQ8ZLupyciHsv0EDVv3jw6duyIn58fiYmJpKen19h36dKlxMfHExYWRmBgIHFxcSxcuPCsPiNGjKBly5ZYLBays7PPOs+wYcOwWCxVHr/73e+q9MnJySEpKYmAgABat27Nn//8Z8rLtbeNSH3d0LctwX7e5Bwt5puQa8DbH/I3Q85as0sTEakXU0PU4sWLSU5OZvbs2WRlZdG3b19GjhzJoUOHqu0fHh7OAw88QFpaGhs3bmTq1KlMnTqV5cuXu/oUFRVx6aWX8uijj57zte+8804OHjzoejz22GOuY3a7naSkJEpLS/nmm2947bXXWLBgAbNmzWqYgYs0QwE2b0b3bw/AgqwC6D3GeUDbHYiIh7IYhnlz6YmJiQwcOJDnnnsOAIfDQXR0NPfeey8zZsyo1Tn69+9PUlISc+bMqdK+Z88eOnXqxIYNG4iLi6tybNiwYcTFxfHMM89Ue87PPvuM66+/ngMHDhAZ6bxtxfPPP8/06dM5fPgwNput2ueVlJRQUlLi+rqwsJDo6GgKCgoICQmp1XhEmrKdh04w/KkvsVpg7dRWtH7zarD6wH1bIDjS7PJERADn7+/Q0NDz/v42bSaqtLSUzMxMhg8ffqYYq5Xhw4eTlpZ23ucbhkFqairbt29n6NChdX79N954g4iICHr16sXMmTMpLi52HUtLS6N3796uAAUwcuRICgsL2bJlS43nTElJITQ01PWIjo6uc10iTVls62AGd26Jw4DXd4dC+wRwlOl+eiLikUwLUUeOHMFut1cJKgCRkZHk5eXV+LyCggKCgoKw2WwkJSUxd+5crr766jq99sSJE1m0aBGrVq1i5syZLFy4kMmTJ7uO5+XlVVtX5bGazJw5k4KCAtcjNze3TnWJNAeV2x28nZFL+YDbnY26n56IeCBvswuoq+DgYLKzszl58iSpqakkJyfTuXNnhg0bVutz3HXXXa6/9+7dmzZt2nDVVVfxww8/0KVLl3rX5uvri6+vb72fL9IcjOgZSatgXw6fKOFzBnFdQAScOADbP4EevzK7PBGRWjNtJioiIgIvLy/y8/OrtOfn5xMVFVXj86xWK7GxscTFxXH//fczZswYUlJSflEtiYmJAOzcuROAqKioauuqPCYi9efjZWX8QOel7tczDsKAW50H0rXAXEQ8i2khymazMWDAAFJTU11tDoeD1NRUBg8eXOvzOByOKou566NyG4Q2bdoAMHjwYDZt2lTlU4IrVqwgJCSEHj16/KLXEhGYkBDjXFy+6yh7Oo4FixX2/A8Obze7NBGRWjN1i4Pk5GRefPFFXnvtNbZu3cr//d//UVRUxNSpUwGYMmUKM2fOdPVPSUlhxYoV7Nq1i61bt/Lkk0+etZ7p6NGjZGdn89133wGwfft2srOzXWuZfvjhB+bMmUNmZiZ79uzhww8/ZMqUKQwdOpQ+ffoAMGLECHr06MEtt9zCt99+y/Lly/nb3/7GtGnTdLlOpAG0DfPnym7OdYYLttjh4mudBzJeMrEqEZG6MTVEjRs3jieeeIJZs2YRFxdHdnY2y5Ytcy3izsnJ4eDBg67+RUVF3H333fTs2ZNLLrmEJUuWsGjRIu644w5Xnw8//JB+/fqRlJQEwPjx4+nXrx/PP/884JwBW7lyJSNGjKBbt27cf//9jB49mo8++sh1Di8vLz7++GO8vLwYPHgwkydPZsqUKTz88MMX4tsi0ixU7mC+JGsfp/s7/+FE9ltQcsLEqkREas/UfaKautruMyHSHDkcBsOeWE3O0WL+eVNPxqePhh93QtKTMPCO859ARKSRuP0+USLSvFmtFiYlOmejFqXnYsRXbHeQrvvpiYhnUIgSEdPcHB+NzdvK5v2FbG6VBD4BcHgr7P3a7NJERM5LIUpETBMeaCOpt/NTsa9tOA59xjoPaLsDEfEAClEiYqrKBeYffXuAwt63ORu3fQyFB2t+koiIG1CIEhFT9Y9pQbeoYErKHbyTGwoxg8FRDpkLzC5NROScFKJExFQWi8V1P7031uXgiK/4ZF7mArCXmVeYiMh5KESJiOlG9WtHkK83u48UkWYbAoGt4WQebP3o/E8WETGJQpSImC7I15ub+rUDYGHGQRhwm/OAdjAXETemECUibqHykt6KrfkcungCWLycWx3kf2dyZSIi1VOIEhG30DUqmIEdW2B3GLy5rRy6Xec8oNkoEXFTClEi4jYqZ6PeTs/FPqBigfnGxXC60MSqRESqpxAlIm7jml5RtAy0kVd4mhWnukJEVyg9Cd++bXZpIiJnUYgSEbfh6+3FzfHRALyRnnPmRsQZup+eiLgfhSgRcSuTEmOwWOB/3x9hT/SNYAuCI9th95dmlyYiUoVClIi4lejwAIZd3AqARVlHoc8454EM3U9PRNyLQpSIuJ3KBebvZu6jpP9vnI3bPoWC/SZWJSJSlUKUiLidYV1b0y7Mn4JTZXx0MAw6XAqGHTJfNbs0EREXhSgRcTteVgsTE2MAWLR2LyRU3k/vNSgvNbEyEZEzFKJExC2NjY/Gx8tCdu5xNodcBsFtoOgQbP3Q7NJERACFKBFxU62CfRnZMwqANzIOnLmfXroWmIuIe1CIEhG3dUvFAvMPNhzgRM+JYPWG3LWQt8nkykREFKJExI0ldArn4sggTpXZWbLDDt2udx7Q/fRExA0oRImI27JYLExKdM5GLVqXg1G5g/nGd+DUcfMKExFBIUpE3NxN/dvh7+PFzkMnWefoDq26Q1kxfPuW2aWJSDOnECUibi3Ez4dR/doCztko13YHGS+Bw2FiZSLS3ClEiYjbq7ykt3xLHkc63wS2YPhxJ+xebW5hItKsKUSJiNvr1S6UuOgwyuwGizceg7gJzgPpWmAuIuZRiBIRj1C53cGb63KwD7jd2bjjMziea2JVItKcKUSJiEdI6tOGsAAf9h8/xaofW0CnoWA4YP0rZpcmIs2UQpSIeAQ/Hy9uHtAegEXr9sLAO50Hsl6H8hITKxOR5kohSkQ8xsSKBeZrdhwmt/UwCGkHxUdgywem1iUizZNClIh4jE4RgVx2UQSGUXk/vanOAxm6n56IXHgKUSLiUSq3O3hnfS4lfSeB1Qf2ZcCBbHMLE5FmRyFKRDzK8O6tiQrx42hRKcv2GNDjRucB3U9PRC4whSgR8SjeXlbGJ0QDsGjtTxaYb3oPTh0zsTIRaW4UokTE40xIiMHLaiFjzzG22XpAZC8oPwUb3jC7NBFpRhSiRMTjRIb4MaJHJFBxP72Bup+eiFx4ClEi4pEmV+xg/n7Wfk52/TX4hsKx3fDDFyZXJiLNhUKUiHikIV1a0jkikKJSOx9sOQ5xE50HtN2BiFwgpoeoefPm0bFjR/z8/EhMTCQ9Pb3GvkuXLiU+Pp6wsDACAwOJi4tj4cKFZ/UZMWIELVu2xGKxkJ2dXeX40aNHuffee+natSv+/v7ExMTw+9//noKCgir9LBbLWY+33367wcYtIr+MxWJhYmIM4FxgbgysvJ/ecji2x7zCRKTZMDVELV68mOTkZGbPnk1WVhZ9+/Zl5MiRHDp0qNr+4eHhPPDAA6SlpbFx40amTp3K1KlTWb58uatPUVERl156KY8++mi15zhw4AAHDhzgiSeeYPPmzSxYsIBly5Zx++23n9X31Vdf5eDBg67HqFGjGmTcItIwxgxoj6+3lW15J8gqagmdrwAM3U9PRC4Ii2EYhlkvnpiYyMCBA3nuuecAcDgcREdHc++99zJjxoxanaN///4kJSUxZ86cKu179uyhU6dObNiwgbi4uHOe491332Xy5MkUFRXh7e0NOP+V+/777/+i4FRYWEhoaCgFBQWEhITU+zwiUrM/vfst72Xu46Z+7Xi67354eyL4h0PyVvDxM7s8EfFAtf39bdpMVGlpKZmZmQwfPvxMMVYrw4cPJy0t7bzPNwyD1NRUtm/fztChQ39RLZXfpMoAVWnatGlERESQkJDAK6+8wvnyZklJCYWFhVUeItK4bqlYYP7JxoP82PYKCI2GU0dhy1KTKxORps60EHXkyBHsdjuRkZFV2iMjI8nLy6vxeQUFBQQFBWGz2UhKSmLu3LlcffXVv6iOOXPmcNddd1Vpf/jhh3nnnXdYsWIFo0eP5u6772bu3LnnPFdKSgqhoaGuR3R0dL3rEpHa6RsdRu92oZTaHby74SDEV9xPL10LzEWkcZm+sLyugoODyc7OJiMjg3/84x8kJyezevXqep2rsLCQpKQkevTowUMPPVTl2IMPPsgll1xCv379mD59On/5y194/PHHz3m+mTNnUlBQ4Hrk5ubWqy4RqZvJg5wLzN9cl4Mj7hbwssGBLNifaXJlItKUmRaiIiIi8PLyIj8/v0p7fn4+UVFRNT7ParUSGxtLXFwc999/P2PGjCElJaXOr3/ixAmuueYagoODef/99/Hx8Tln/8TERPbt20dJSUmNfXx9fQkJCanyEJHGd0PftgT7eZNztJgvDwA9RjkPZLxsZlki0sSZFqJsNhsDBgwgNTXV1eZwOEhNTWXw4MG1Po/D4ThnsKlOYWEhI0aMwGaz8eGHH+Lnd/7Fp9nZ2bRo0QJfX986vZaINL4Amzej+7cHYNHaHEiouJ/e5iVQfNTEykSkKfM+f5fGk5yczK233kp8fDwJCQk888wzFBUVMXWqc03DlClTaNeunWumKSUlhfj4eLp06UJJSQmffvopCxcuZP78+a5zHj16lJycHA4cOADA9u3bAYiKiiIqKsoVoIqLi1m0aFGVBeCtWrXCy8uLjz76iPz8fAYNGoSfnx8rVqzgkUce4U9/+tOF/PaISB1MHhTDgm/28MW2fPbfeAXtovpA3kbYsBAu+YPZ5YlIE2RqiBo3bhyHDx9m1qxZ5OXlERcXx7Jly1yLzXNycrBaz0yWFRUVcffdd7Nv3z78/f3p1q0bixYtYty4ca4+H374oSuEAYwfPx6A2bNn89BDD5GVlcW6desAiI2NrVLP7t276dixIz4+PsybN4/77rsPwzCIjY3lqaee4s4772y074WI/DKxrYMZ1DmctbuO8nZGLvcn3Akf3uu8pDf4HrB6mV2iiDQxpu4T1dRpnyiRC+uTjQeZ9mYWEUG+fHP/IGzP9oTTx2HiO3DxSLPLExEP4fb7RImINLQRPSNpFezLkZMlfP59IfSb7Dyg7Q5EpBEoRIlIk+HjZWX8QOf+bIvW7oX43zgP7FwJR3eZWJmINEUKUSLSpExIiMFqgbW7jrLT3hpihwOGtjsQkQanECUiTUrbMH+u7Ob8cMqitTkwsOIDIRsWQWmxiZWJSFOjECUiTU7lDuZLsvZR3OEKCItxLjDfvMTcwkSkSVGIEpEmZ+hFrYgJD+DE6XI+2pQP8bc7D2S8CPpAsog0EIUoEWlyrFYLkxKds1EL1+7F6DcZvHzh4Lewb73J1YlIU6EQJSJN0s3x0di8rWzeX8i3R72h16+dBzJeMrcwEWkyFKJEpEkKD7SR1LsNULHdQeUC8y1LoeiIiZWJSFOhECUiTVblAvOPvj3A8fDe0LYf2Esh63WTKxORpkAhSkSarP4xLegWFUxJuYP3MvedmY1a/yo47OYWJyIeTyFKRJosi8XC5EEdAHhzXQ5Gz5vAvwUU5MCO5SZXJyKeTiFKRJq0Uf3aEWjzYteRIr7JKYZ+tzgPZOh+eiLyyyhEiUiTFuTrza/7twdgYdpeGHg7YIEfvoAjO80tTkQ8mkKUiDR5lZf0VmzNJ88aBReNcB5Yr/vpiUj9KUSJSJPXNSqYgR1bYHcYvJ2RAwmV99N7A0qLzC1ORDyWQpSINAuVs1Fvp+dS3ukKaNEJSgpg07smVyYinkohSkSahWt6RdEy0EZe4WlWbjtSsTYKSH9J99MTkXpRiBKRZsHX24ub46MBeGPdXoibBN5+kL8JcteZXJ2IeCKFKBFpNiYlxmCxwP++P8LuYl/oNcZ5QPfTE5F6UIgSkWYjOjyAYRe3AuCNtXsh4Q7ngS0fwMlD5hUmIh5JIUpEmpXKBebvZu7jdKs+0C4eHGWQ9ZrJlYmIp1GIEpFmZVjX1rQL86fgVBkfbzx4ZruD9a+Cvdzc4kTEoyhEiUiz4mW1MDExBoBFa/dCj1EQ0BIK98OOz8wtTkQ8ikKUiDQ7Y+Oj8fGykJ17nM2HSqD/FOeBdN1PT0RqTyFKRJqdVsG+jOwZBVRsdxD/G7BYYfcaOLzD5OpExFMoRIlIs1S5wPyDDQco9GsDF1/jPKDtDkSklhSiRKRZSuwUzkWtgzhVZmdp5j4YWLHdwbdvQclJc4sTEY+gECUizZLFYnHNRi1al4PReRiEd4GSQti42NziRMQjKESJSLN1U/92+Pt4sfPQSdbtOX5mNipD99MTkfNTiBKRZivEz4dR/doCFdsdxE0Ab3849B3s/cbk6kTE3SlEiUizNinReUlv+ZY8DpcHQJ+bnQe0wFxEzkMhSkSatV7tQomLDqPMbvDO+lwYWLGD+dYP4USeucWJiFtTiBKRZq9ygfmb63KwR/aG6ERwlEOm7qcnIjVTiBKRZu/6Pm0IC/Bh//FTrNp26MxsVOarYC8ztzgRcVsKUSLS7Pn5eHHzgPYALFq3F3rcCIGt4MRB2PaJydWJiLtSiBIRASZWLDBfs+MwuYV26H+r84AWmItIDRSiRESAThGBXHZRBIYBb6zLgfipzvvp7fkfHNpqdnki4oYUokREKlRud/DO+lxKAttA1+ucBzQbJSLVMD1EzZs3j44dO+Ln50diYiLp6ek19l26dCnx8fGEhYURGBhIXFwcCxcuPKvPiBEjaNmyJRaLhezs7LPOc/r0aaZNm0bLli0JCgpi9OjR5OfnV+mTk5NDUlISAQEBtG7dmj//+c+Ul5c3yJhFxD0N796aqBA/jhaVsmxzHiRULDD/9m04XWhucSLidkwNUYsXLyY5OZnZs2eTlZVF3759GTlyJIcOHaq2f3h4OA888ABpaWls3LiRqVOnMnXqVJYvX+7qU1RUxKWXXsqjjz5a4+ved999fPTRR7z77rusWbOGAwcO8Otf/9p13G63k5SURGlpKd988w2vvfYaCxYsYNasWQ03eBFxO95eVsYnRAMVO5h3uhxaXgSlJ3U/PRE5m2GihIQEY9q0aa6v7Xa70bZtWyMlJaXW5+jXr5/xt7/97az23bt3G4CxYcOGKu3Hjx83fHx8jHfffdfVtnXrVgMw0tLSDMMwjE8//dSwWq1GXl6eq8/8+fONkJAQo6SkpNa1FRQUGIBRUFBQ6+eIiLnyCk4ZnWd+YnSY/rGx9WCBYaTNN4zZIYYxd6BhOBxmlyciF0Btf3+bNhNVWlpKZmYmw4cPd7VZrVaGDx9OWlraeZ9vGAapqals376doUOH1vp1MzMzKSsrq/K63bp1IyYmxvW6aWlp9O7dm8jISFefkSNHUlhYyJYtW2o8d0lJCYWFhVUeIuJZIkP8GNHD+bPvup+eTyAc2e5cZC4iUsG0EHXkyBHsdnuVoAIQGRlJXl7Nt1ooKCggKCgIm81GUlISc+fO5eqrr6716+bl5WGz2QgLC6vxdfPy8qqtq/JYTVJSUggNDXU9oqOja12XiLiPyh3M38/az0lLIPQZ6zygBeYi8hOmLyyvq+DgYLKzs8nIyOAf//gHycnJrF692uyyAJg5cyYFBQWuR25urtkliUg9DOnSks4RgRSV2vlgw/4zC8y3fgyFB8wtTkTchmkhKiIiAi8vr7M+FZefn09UVFSNz7NarcTGxhIXF8f999/PmDFjSElJqfXrRkVFUVpayvHjx2t83aioqGrrqjxWE19fX0JCQqo8RMTzWCwWJibGAM5LekbrHhAzBAw7ZC4wtzgRcRumhSibzcaAAQNITU11tTkcDlJTUxk8eHCtz+NwOCgpKal1/wEDBuDj41Pldbdv305OTo7rdQcPHsymTZuqfEpwxYoVhISE0KNHj1q/loh4rjED2uPrbWVb3gmyco5Bwh3OA5kLoLzU1NpExD14m/niycnJ3HrrrcTHx5OQkMAzzzxDUVERU6dOBWDKlCm0a9fONdOUkpJCfHw8Xbp0oaSkhE8//ZSFCxcyf/581zmPHj1KTk4OBw44p9y3b98OOGeQoqKiCA0N5fbbbyc5OZnw8HBCQkK49957GTx4MIMGDQJgxIgR9OjRg1tuuYXHHnuMvLw8/va3vzFt2jR8fX0v5LdIREwSFmDjhr5teS9zH4vW5jBg9A0QFAkn82HbR9BrtNkliojJTF0TNW7cOJ544glmzZpFXFwc2dnZLFu2zLWIOycnh4MHD7r6FxUVcffdd9OzZ08uueQSlixZwqJFi7jjjjtcfT788EP69etHUlISAOPHj6dfv348//zzrj5PP/00119/PaNHj2bo0KFERUWxdOlS13EvLy8+/vhjvLy8GDx4MJMnT2bKlCk8/PDDjf0tERE3ckvFAvNPNh7kx9MGDLjNeSBdC8xFBCyGYRhmF9FUFRYWEhoaSkFBgdZHiXioG+Z+xab9Bcy4thu/6+cPT/dyro36v28gsqfZ5YlII6jt72+P+3SeiMiFNHmQc4H5m+tycAS1ge7XOw+kv2hiVSLiDhSiRETO4Ya+bQn28ybnaDFffn8YBlZsd7DxHThdYG5xImIqUxeWi4i4uwCbN6P7t2fBN3tYtDaHYVMuhVbd4PA2eLQjWH3A6l3x8Krhz4qHxVr162r7/eTvFq+zz2Gtoc1SwzlqrKUO53b1r+HcFitYLGa/VSIXnEKUiMh5TB4Uw4Jv9vDFtnz2F5ym3WV/gqV3guEAe4nz0dzVOaBdqLD48+D6k3N72cDHH3wCfvJnxd9tgeDlY/Z3VdycQpSIyHnEtg5mUOdw1u46ytvpOdw/4ma4eASUFoOjvOJhP/N3w352m6McHI6ffV3Rx7BXfx5HDecxajhPtc+trOfntVRXj/3c9RuOmr9JlX2aEqt31WDlClg/bQus+LMieP08jP00lFX28/lJPy/9GvZkevdERGrhlkEdWbvrKG+l53LvlRdh8wsFv1Czy7qwDKOaMPbzEFhd+KtNwKwu6NXx3DUGxhoCpr0Uyk5BWfFP/iw+ExYd5VBS6Hw0lrNmw6oLZT8LXjUGuYCK9p/Nrlm9Gq/+Zk4hSkSkFkb0jKRVsC+HT5Tw+Xd5XN+nrdklXXgWi3PmpCnPnhiGM1yVFlUEq5+Eq8qwVVp8dvCq/Pt5j1X8nYrdheylzkdjfkjBy7eeoezns2vVXPL08Qdvf7A2z8+pNeGfBBGRhuPjZWX8wGjmfrGTRWv3Ns8Q1RxYLODt63w0FsOA8tNVZ8Fcoe1coewUlBVVH8qqe26lynV7p4833pi8/c8RyqoJXlXazzO7VtnfDT+8oBAlIlJLExJimLdqJ2t3HWXnoRPEtg42uyTxRBbLmWBAeOO8hsPxk6BW9LPA9tOwda5jP519q2Zmrvz0mdcrP+V8nDraOOOBmmfKJr3nDGAmUIgSEamltmH+XNktkpVb81m0NoeHbtSO5eKmrFZnsLAFAC0b5zUcDmdwaojLm2U/m3GrDG0//eRrZR9+rFqHl61xxlcLClEiInUweVAMK7fmsyRrH3+5pisBNv1vVJopq9U5E2QLbLzXcNjPEbwq/jRxjZ5++kVE6mDoRa2ICQ8g52gxlz66Cm9r7ddp1OdGpXW/u2ndX6Wur1HXV6jPLVrr/hp1fgm8rBaC/bwJ9vMmxM/nJ3/6EOLv7fzTz9v1dYifj6tfsJ833l7NczH1BWX1At9g58MNKUSJiNSB1Wrhjss6Meu/WzhaVGp2OfIL/ZL3MMDmVSVUhfj7nBW8Kr8OqfK18zkBNi8sbrhYWmpPIUpEpI5uGdSBS2MjOFVmr/NzLdTtl2Z9fsfW9Tl1ral+r1F3dR973Z5Q7nBw4nQ5J06XceJ0OYWnyig8XU7hT74+Uc3Xle97camd4lI7efXcRqq2M2HVBbDgiqBm89ZsmJkUokRE6shisdC5VZDZZYhJyuxnwlfhqYo/T1cEsBqC14mSn/Ytx+4wsDsMjheXcby4DDhVr1r8fKxnAlg1M2Eh1cyM/TS0Bfl6azbsF1CIEhERqQMfLyvhgTbCA+v3qTDDMDhVZq8xgJ0JYVWD10+/Lip1zoadLnNwuqyEQyfqd/9GqwWCfOsXwJzP8cbXu/nuiK4QJSIicgFZLBYCbN4E2LyJCvWr1znK7Q5OljgDV0ENs19VgljJ2cfK7AYOg4pLmOXUdzbM19t6zkuQwb5nAtfPA1iIvw9BNm+sdfiAhjtRiBIREfEw3l5WwgJshAXYiK7H8w3DoKTccZ51YOdeG3aixHnD6ZJyByUnSzhysn6zYZbK2bCfzHSda+br519HBPqaFsIUokRERJoZi8WCn48Xfj5etA6p3znsDoOTJT+9DPnTy44/C15nBTJnW2m5A8PAdRmzPjb/fSRBvubEGYUoERERqTMvq4VQfx9C/X3qfY7TZfZqAtiZtWKuNWLVzpiVUVxqJ9Bm3poshSgRERExReVsWKvg+t3w2eEwTP10oTaYEBEREY9k9oJ0hSgRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRERGRevA2u4CmzDAMAAoLC02uRERERGqr8vd25e/xmihENaITJ04AEB0dbXIlIiIiUlcnTpwgNDS0xuMW43wxS+rN4XBw4MABgoODsVgsDXbewsJCoqOjyc3NJSQkpMHO6y6a+vig6Y+xqY8Pmv4YNT7P19TH2JjjMwyDEydO0LZtW6zWmlc+aSaqEVmtVtq3b99o5w8JCWmSPxiVmvr4oOmPsamPD5r+GDU+z9fUx9hY4zvXDFQlLSwXERERqQeFKBEREZF6UIjyQL6+vsyePRtfX1+zS2kUTX180PTH2NTHB01/jBqf52vqY3SH8WlhuYiIiEg9aCZKREREpB4UokRERETqQSFKREREpB4UokRERETqQSHKTc2bN4+OHTvi5+dHYmIi6enp5+z/7rvv0q1bN/z8/OjduzeffvrpBaq0fuoyvgULFmCxWKo8/Pz8LmC1dfPll19yww030LZtWywWCx988MF5n7N69Wr69++Pr68vsbGxLFiwoNHr/CXqOsbVq1ef9R5aLBby8vIuTMF1lJKSwsCBAwkODqZ169aMGjWK7du3n/d5nvJzWJ/xedLP4fz58+nTp49rE8bBgwfz2WefnfM5nvLeVarrGD3p/avOP//5TywWC3/84x/P2e9Cv48KUW5o8eLFJCcnM3v2bLKysujbty8jR47k0KFD1fb/5ptvmDBhArfffjsbNmxg1KhRjBo1is2bN1/gymunruMD5460Bw8edD327t17ASuum6KiIvr27cu8efNq1X/37t0kJSVxxRVXkJ2dzR//+EfuuOMOli9f3siV1l9dx1hp+/btVd7H1q1bN1KFv8yaNWuYNm0aa9euZcWKFZSVlTFixAiKiopqfI4n/RzWZ3zgOT+H7du355///CeZmZmsX7+eK6+8kl/96lds2bKl2v6e9N5VqusYwXPev5/LyMjghRdeoE+fPufsZ8r7aIjbSUhIMKZNm+b62m63G23btjVSUlKq7T927FgjKSmpSltiYqLx29/+tlHrrK+6ju/VV181QkNDL1B1DQsw3n///XP2+ctf/mL07NmzStu4ceOMkSNHNmJlDac2Y1y1apUBGMeOHbsgNTW0Q4cOGYCxZs2aGvt42s/hT9VmfJ78c2gYhtGiRQvjpZdeqvaYJ793P3WuMXrq+3fixAnjoosuMlasWGFcfvnlxh/+8Ica+5rxPmomys2UlpaSmZnJ8OHDXW1Wq5Xhw4eTlpZW7XPS0tKq9AcYOXJkjf3NVJ/xAZw8eZIOHToQHR193n9teRpPev9+qbi4ONq0acPVV1/N119/bXY5tVZQUABAeHh4jX08+X2szfjAM38O7XY7b7/9NkVFRQwePLjaPp783kHtxgie+f5NmzaNpKSks96f6pjxPipEuZkjR45gt9uJjIys0h4ZGVnj+pG8vLw69TdTfcbXtWtXXnnlFf773/+yaNEiHA4HQ4YMYd++fRei5EZX0/tXWFjIqVOnTKqqYbVp04bnn3+eJUuWsGTJEqKjoxk2bBhZWVlml3ZeDoeDP/7xj1xyySX06tWrxn6e9HP4U7Udn6f9HG7atImgoCB8fX353e9+x/vvv0+PHj2q7eup711dxuhp7x/A22+/TVZWFikpKbXqb8b76N1oZxZpIIMHD67yr6shQ4bQvXt3XnjhBebMmWNiZVJbXbt2pWvXrq6vhwwZwg8//MDTTz/NwoULTazs/KZNm8bmzZv56quvzC6lUdR2fJ72c9i1a1eys7MpKCjgvffe49Zbb2XNmjU1hgxPVJcxetr7l5ubyx/+8AdWrFjh1gvgFaLcTEREBF5eXuTn51dpz8/PJyoqqtrnREVF1am/meozvp/z8fGhX79+7Ny5szFKvOBqev9CQkLw9/c3qarGl5CQ4PbB5J577uHjjz/myy+/pH379ufs60k/h5XqMr6fc/efQ5vNRmxsLAADBgwgIyODZ599lhdeeOGsvp743kHdxvhz7v7+ZWZmcujQIfr37+9qs9vtfPnllzz33HOUlJTg5eVV5TlmvI+6nOdmbDYbAwYMIDU11dXmcDhITU2t8Vr34MGDq/QHWLFixTmvjZulPuP7ObvdzqZNm2jTpk1jlXlBedL715Cys7Pd9j00DIN77rmH999/ny+++IJOnTqd9zme9D7WZ3w/52k/hw6Hg5KSkmqPedJ7dy7nGuPPufv7d9VVV7Fp0yays7Ndj/j4eCZNmkR2dvZZAQpMeh8bbcm61Nvbb79t+Pr6GgsWLDC+++4746677jLCwsKMvLw8wzAM45ZbbjFmzJjh6v/1118b3t7exhNPPGFs3brVmD17tuHj42Ns2rTJrCGcU13H9/e//91Yvny58cMPPxiZmZnG+PHjDT8/P2PLli1mDeGcTpw4YWzYsMHYsGGDARhPPfWUsWHDBmPv3r2GYRjGjBkzjFtuucXVf9euXUZAQIDx5z//2di6dasxb948w8vLy1i2bJlZQzivuo7x6aefNj744APj+++/NzZt2mT84Q9/MKxWq7Fy5UqzhnBO//d//2eEhoYaq1evNg4ePOh6FBcXu/p48s9hfcbnST+HM2bMMNasWWPs3r3b2LhxozFjxgzDYrEYn3/+uWEYnv3eVarrGD3p/avJzz+d5w7vo0KUm5o7d64RExNj2Gw2IyEhwVi7dq3r2OWXX27ceuutVfq/8847xsUXX2zYbDajZ8+exieffHKBK66buozvj3/8o6tvZGSkcd111xlZWVkmVF07lR/n//mjcky33nqrcfnll5/1nLi4OMNmsxmdO3c2Xn311Qted13UdYyPPvqo0aVLF8PPz88IDw83hg0bZnzxxRfmFF8L1Y0NqPK+ePLPYX3G50k/h7/5zW+MDh06GDabzWjVqpVx1VVXucKFYXj2e1eprmP0pPevJj8PUe7wPloMwzAab55LREREpGnSmigRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRERGRelCIEhEREakHhSgRkQtk9erVWCwWjh8/bnYpItIAFKJERERE6kEhSkRERKQeFKJEpNlwOBykpKTQqVMn/P396du3L++99x5w5lLbJ598Qp8+ffDz82PQoEFs3ry5yjmWLFlCz5498fX1pWPHjjz55JNVjpeUlDB9+nSio6Px9fUlNjaWl19+uUqfzMxM4uPjCQgIYMiQIWzfvr1xBy4ijUIhSkSajZSUFF5//XWef/55tmzZwn333cfkyZNZs2aNq8+f//xnnnzySTIyMmjVqhU33HADZWVlgDP8jB07lvHjx7Np0yYeeughHnzwQRYsWOB6/pQpU3jrrbf417/+xdatW3nhhRcICgqqUscDDzzAk08+yfr16/H29uY3v/nNBRm/iDQsi2EYhtlFiIg0tpKSEsLDw1m5ciWDBw92td9xxx0UFxdz1113ccUVV/D2228zbtw4AI4ePUr79u1ZsGABY8eOZdKkSRw+fJjPP//c9fy//OUvfPLJJ2zZsoUdO3bQtWtXVqxYwfDhw8+qYfXq1VxxxRWsXLmSq666CoBPP/2UpKQkTp06hZ+fXyN/F0SkIWkmSkSahZ07d1JcXMzVV19NUFCQ6/H666/zww8/uPr9NGCFh4fTtWtXtm7dCsDWrVu55JJLqpz3kksu4fvvv8dut5OdnY2XlxeXX375OWvp06eP6+9t2rQB4NChQ794jCJyYXmbXYCIyIVw8uRJAD755BPatWtX5Zivr2+VIFVf/v7+tern4+Pj+rvFYgGc67VExLNoJkpEmoUePXrg6+tLTk4OsbGxVR7R0dGufmvXrnX9/dixY+zYsYPu3bsD0L17d77++usq5/3666+5+OKL8fLyonfv3jgcjiprrESk6dJMlIg0C8HBwfzpT3/ivvvuw+FwcOmll1JQUMDXX39NSEgIHTp0AODhhx+mZcuWREZG8sADDxAREcGoUaMAuP/++xk4cCBz5sxh3LhxpKWl8dxzz/Hvf/8bgI4dO3Lrrbfym9/8hn/961/07duXvXv3cujQIcaOHWvW0EWkkShEiUizMWfOHFq1akVKSgq7du0iLCyM/v3789e//tV1Oe2f//wnf/jDH/j++++Ji4vjo48+wmazAdC/f3/eeecdZs2axZw5c2jTpg0PP/wwt912m+s15s+fz1//+lfuvvtufvzxR2JiYvjrX/9qxnBFpJHp03kiIpz55NyxY8cICwszuxwR8QBaEyUiIiJSDwpRIiIiIvWgy3kiIiIi9aCZKBEREZF6UIgSERERqQeFKBEREZF6UIgSERERqQeFKBEREZF6UIgSERERqQeFKBEREZF6UIgSERERqYf/D/FEwSmIDnYiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr: float = 4e-2\n",
    "n_epoch: int = 3\n",
    "n_splits: int = 3\n",
    "batch_size: int = 512\n",
    "verbose: bool = True\n",
    "\n",
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "if dataset.empty:\n",
    "    raise ValueError('Training data is inadequate.')\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "n_pre_train_groups = len(dataset[dataset['i'] == 2]['group'].unique())\n",
    "if n_pre_train_groups < n_splits:\n",
    "    print(\"Not enough groups for pre-training. Splitting into {} folds.\".format(n_pre_train_groups))\n",
    "    n_splits = n_pre_train_groups\n",
    "\n",
    "w = []\n",
    "plots = []\n",
    "if n_splits > 1:\n",
    "    sgkf = StratifiedGroupKFold(n_splits=n_splits)\n",
    "    for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "        print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "        train_set = dataset.iloc[train_index].copy()\n",
    "        test_set = dataset.iloc[test_index].copy()\n",
    "        trainer = Trainer(train_set, test_set, init_w, n_epoch=n_epoch, lr=lr, batch_size=batch_size)\n",
    "        w.append(trainer.train(verbose=verbose))\n",
    "        plots.append(trainer.plot())\n",
    "else:\n",
    "    trainer = Trainer(dataset, dataset, init_w, n_epoch=n_epoch, lr=lr, batch_size=batch_size)\n",
    "    w.append(trainer.train(verbose=verbose))\n",
    "    plots.append(trainer.plot())\n",
    "\n",
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "w = avg_w.tolist()\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4684, 2.0442, 4.8683, -1.9262, -1.8467, 0.05, 1.5112, -0.2049, 0.8812, 2.0184, -0.2452, 0.3511, 0.6359]\n",
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,1d,2d,4d,8d,14d,25d,1.4m,2.4m,3.9m,6.1m\n",
      "difficulty history: 0,8.7,8.5,8.3,8.2,8.0,7.8,7.7,7.6,7.4,7.3\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,4d,9d,20d,1.3m,2.5m,4.5m,7.8m,1.1y,1.7y,2.6y\n",
      "difficulty history: 0,6.8,6.7,6.6,6.5,6.4,6.4,6.3,6.2,6.1,6.1\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,6d,16d,1.3m,2.9m,5.8m,11.0m,1.6y,2.7y,4.4y,6.9y\n",
      "difficulty history: 0,4.9,4.9,4.9,4.9,4.9,4.9,4.9,4.9,4.9,4.9\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,8d,25d,2.3m,5.4m,11.5m,1.8y,3.4y,5.8y,9.6y,15.1y\n",
      "difficulty history: 0,2.9,3.0,3.1,3.2,3.3,3.4,3.5,3.5,3.6,3.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(w)\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w: List[float]) -> None:\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history: str, r_history: str):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "\n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        with torch.no_grad():\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "        \n",
    "requestRetention = 0.9\n",
    "\n",
    "my_collection = Collection(w)\n",
    "preview_text = \"1:again, 2:hard, 3:good, 4:easy\\n\"\n",
    "for first_rating in (1,2,3,4):\n",
    "    preview_text += f'\\nfirst rating: {first_rating}\\n'\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    preview_text += f\"rating history: {r_history}\\n\"\n",
    "    preview_text += \"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]) + \"\\n\"\n",
    "    preview_text += f\"difficulty history: {d_history}\\n\"\n",
    "print(preview_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([5.5568, 4.8683])\n",
      "tensor([16.3573,  4.8683])\n",
      "tensor([39.4650,  4.8683])\n",
      "tensor([86.4853,  4.8683])\n",
      "tensor([174.7686,   4.8683])\n",
      "tensor([7.8279, 8.3770])\n",
      "tensor([ 2.5221, 10.0000])\n",
      "tensor([3.8218, 9.7434])\n",
      "tensor([5.7247, 9.4997])\n",
      "tensor([8.7577, 9.2681])\n",
      "tensor([13.4582,  9.0481])\n",
      "tensor([20.3454,  8.8391])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,6,16,39,86,175,8,3,4,6,9,13,20\n",
      "difficulty history: 0,4.9,4.9,4.9,4.9,4.9,8.4,10.0,9.7,9.5,9.3,9.0,8.8\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "preview_text = f\"rating history: {test_rating_sequence}\\n\"\n",
    "preview_text += f\"interval history: {t_history}\\n\"\n",
    "preview_text += f\"difficulty history: {d_history}\"\n",
    "print(preview_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss before: 0.3251, loss after: 0.3086, improvement: 0.0165\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "loss_before = dataset['log_loss'].mean()\n",
    "\n",
    "my_collection = Collection(w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "loss_after = dataset['log_loss'].mean()\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp\n",
    "print(f\"loss before: {loss_before:.4f}, loss after: {loss_after:.4f}, improvement: {loss_before - loss_after:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9743\n",
      "RMSE: 0.0113\n",
      "[0.05813878 0.93606851]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Last rating: 1\n",
      "R-squared: 0.5503\n",
      "RMSE: 0.0350\n",
      "[0.20821536 0.74866464]\n",
      "\n",
      "Last rating: 2\n",
      "R-squared: -0.6540\n",
      "RMSE: 0.0969\n",
      "[-0.61924684  1.57341161]\n",
      "\n",
      "Last rating: 3\n",
      "R-squared: 0.9523\n",
      "RMSE: 0.0170\n",
      "[0.01931662 0.99141292]\n",
      "\n",
      "Last rating: 4\n",
      "R-squared: -0.1283\n",
      "RMSE: 0.0371\n",
      "[0.45693396 0.53520928]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()\n",
    "plt.figure(figsize=(16, 12))\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    plt.subplot(2, 2, int(last_rating))\n",
    "    print(f\"\\nLast rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c2e95_row0_col0, #T_c2e95_row0_col1, #T_c2e95_row0_col2, #T_c2e95_row0_col3, #T_c2e95_row0_col4, #T_c2e95_row1_col0, #T_c2e95_row1_col1, #T_c2e95_row1_col2, #T_c2e95_row1_col3, #T_c2e95_row2_col0, #T_c2e95_row2_col1, #T_c2e95_row2_col2, #T_c2e95_row3_col0, #T_c2e95_row3_col1, #T_c2e95_row3_col2, #T_c2e95_row4_col0, #T_c2e95_row4_col2, #T_c2e95_row5_col2, #T_c2e95_row6_col2, #T_c2e95_row8_col2, #T_c2e95_row11_col2, #T_c2e95_row11_col6, #T_c2e95_row12_col2, #T_c2e95_row12_col5, #T_c2e95_row12_col6, #T_c2e95_row13_col2, #T_c2e95_row13_col3, #T_c2e95_row13_col4, #T_c2e95_row13_col5, #T_c2e95_row13_col6, #T_c2e95_row14_col0, #T_c2e95_row14_col2, #T_c2e95_row14_col3, #T_c2e95_row14_col4, #T_c2e95_row14_col5, #T_c2e95_row14_col6 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row0_col5 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row0_col6, #T_c2e95_row7_col2, #T_c2e95_row10_col4 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row1_col4 {\n",
       "  background-color: #ffdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row1_col5 {\n",
       "  background-color: #8989ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row1_col6, #T_c2e95_row2_col6, #T_c2e95_row7_col1, #T_c2e95_row12_col4 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row2_col3 {\n",
       "  background-color: #ff9191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row2_col4, #T_c2e95_row10_col2 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row2_col5 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row3_col3, #T_c2e95_row8_col3 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row3_col4, #T_c2e95_row7_col0, #T_c2e95_row9_col0 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row3_col5 {\n",
       "  background-color: #adadff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row3_col6, #T_c2e95_row7_col3, #T_c2e95_row7_col5 {\n",
       "  background-color: #ffc5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row4_col1, #T_c2e95_row4_col3 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row4_col4, #T_c2e95_row11_col5 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row4_col5 {\n",
       "  background-color: #d1d1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row4_col6 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row5_col0, #T_c2e95_row6_col0, #T_c2e95_row11_col0 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row5_col1, #T_c2e95_row8_col1 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row5_col3 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row5_col4, #T_c2e95_row9_col4 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row5_col5 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row5_col6 {\n",
       "  background-color: #ff8585;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row6_col1 {\n",
       "  background-color: #c9c9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row6_col3 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row6_col4 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row6_col5 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row6_col6 {\n",
       "  background-color: #ff5959;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row7_col4 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row7_col6 {\n",
       "  background-color: #f80000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row8_col0, #T_c2e95_row12_col0 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row8_col4 {\n",
       "  background-color: #ffa1a1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row8_col5, #T_c2e95_row9_col2 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row8_col6 {\n",
       "  background-color: #d30000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row9_col1 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row9_col3 {\n",
       "  background-color: #ffb1b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row9_col5 {\n",
       "  background-color: #ffa9a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row9_col6 {\n",
       "  background-color: #ff6161;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row10_col0 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row10_col1 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row10_col3 {\n",
       "  background-color: #ff7171;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row10_col5 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row10_col6 {\n",
       "  background-color: #de0000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row11_col1 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row11_col3 {\n",
       "  background-color: #ff6565;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row11_col4 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row12_col1 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row12_col3 {\n",
       "  background-color: #ff6969;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row13_col0 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c2e95_row13_col1 {\n",
       "  background-color: #7d7dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c2e95_row14_col1 {\n",
       "  background-color: #9d9dff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c2e95\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_c2e95_level0_col0\" class=\"col_heading level0 col0\" >3</th>\n",
       "      <th id=\"T_c2e95_level0_col1\" class=\"col_heading level0 col1\" >5</th>\n",
       "      <th id=\"T_c2e95_level0_col2\" class=\"col_heading level0 col2\" >6</th>\n",
       "      <th id=\"T_c2e95_level0_col3\" class=\"col_heading level0 col3\" >7</th>\n",
       "      <th id=\"T_c2e95_level0_col4\" class=\"col_heading level0 col4\" >8</th>\n",
       "      <th id=\"T_c2e95_level0_col5\" class=\"col_heading level0 col5\" >9</th>\n",
       "      <th id=\"T_c2e95_level0_col6\" class=\"col_heading level0 col6\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row0\" class=\"row_heading level0 row0\" >1.400000</th>\n",
       "      <td id=\"T_c2e95_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_c2e95_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_c2e95_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_c2e95_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_c2e95_row0_col5\" class=\"data row0 col5\" >2.46%</td>\n",
       "      <td id=\"T_c2e95_row0_col6\" class=\"data row0 col6\" >1.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row1\" class=\"row_heading level0 row1\" >1.960000</th>\n",
       "      <td id=\"T_c2e95_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_c2e95_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_c2e95_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_c2e95_row1_col4\" class=\"data row1 col4\" >1.40%</td>\n",
       "      <td id=\"T_c2e95_row1_col5\" class=\"data row1 col5\" >-4.62%</td>\n",
       "      <td id=\"T_c2e95_row1_col6\" class=\"data row1 col6\" >-0.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row2\" class=\"row_heading level0 row2\" >2.740000</th>\n",
       "      <td id=\"T_c2e95_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_c2e95_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_c2e95_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row2_col3\" class=\"data row2 col3\" >4.33%</td>\n",
       "      <td id=\"T_c2e95_row2_col4\" class=\"data row2 col4\" >-0.64%</td>\n",
       "      <td id=\"T_c2e95_row2_col5\" class=\"data row2 col5\" >-0.89%</td>\n",
       "      <td id=\"T_c2e95_row2_col6\" class=\"data row2 col6\" >-0.37%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row3\" class=\"row_heading level0 row3\" >3.840000</th>\n",
       "      <td id=\"T_c2e95_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_c2e95_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_c2e95_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row3_col3\" class=\"data row3 col3\" >-1.24%</td>\n",
       "      <td id=\"T_c2e95_row3_col4\" class=\"data row3 col4\" >-2.01%</td>\n",
       "      <td id=\"T_c2e95_row3_col5\" class=\"data row3 col5\" >-3.16%</td>\n",
       "      <td id=\"T_c2e95_row3_col6\" class=\"data row3 col6\" >2.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row4\" class=\"row_heading level0 row4\" >5.380000</th>\n",
       "      <td id=\"T_c2e95_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_c2e95_row4_col1\" class=\"data row4 col1\" >-1.04%</td>\n",
       "      <td id=\"T_c2e95_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row4_col3\" class=\"data row4 col3\" >-0.98%</td>\n",
       "      <td id=\"T_c2e95_row4_col4\" class=\"data row4 col4\" >-2.68%</td>\n",
       "      <td id=\"T_c2e95_row4_col5\" class=\"data row4 col5\" >-1.83%</td>\n",
       "      <td id=\"T_c2e95_row4_col6\" class=\"data row4 col6\" >0.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row5\" class=\"row_heading level0 row5\" >7.530000</th>\n",
       "      <td id=\"T_c2e95_row5_col0\" class=\"data row5 col0\" >-1.29%</td>\n",
       "      <td id=\"T_c2e95_row5_col1\" class=\"data row5 col1\" >-1.42%</td>\n",
       "      <td id=\"T_c2e95_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row5_col3\" class=\"data row5 col3\" >-0.01%</td>\n",
       "      <td id=\"T_c2e95_row5_col4\" class=\"data row5 col4\" >0.12%</td>\n",
       "      <td id=\"T_c2e95_row5_col5\" class=\"data row5 col5\" >-1.71%</td>\n",
       "      <td id=\"T_c2e95_row5_col6\" class=\"data row5 col6\" >4.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row6\" class=\"row_heading level0 row6\" >10.540000</th>\n",
       "      <td id=\"T_c2e95_row6_col0\" class=\"data row6 col0\" >-1.36%</td>\n",
       "      <td id=\"T_c2e95_row6_col1\" class=\"data row6 col1\" >-2.09%</td>\n",
       "      <td id=\"T_c2e95_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row6_col3\" class=\"data row6 col3\" >1.75%</td>\n",
       "      <td id=\"T_c2e95_row6_col4\" class=\"data row6 col4\" >0.35%</td>\n",
       "      <td id=\"T_c2e95_row6_col5\" class=\"data row6 col5\" >1.10%</td>\n",
       "      <td id=\"T_c2e95_row6_col6\" class=\"data row6 col6\" >6.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row7\" class=\"row_heading level0 row7\" >14.760000</th>\n",
       "      <td id=\"T_c2e95_row7_col0\" class=\"data row7 col0\" >-2.01%</td>\n",
       "      <td id=\"T_c2e95_row7_col1\" class=\"data row7 col1\" >-0.37%</td>\n",
       "      <td id=\"T_c2e95_row7_col2\" class=\"data row7 col2\" >1.64%</td>\n",
       "      <td id=\"T_c2e95_row7_col3\" class=\"data row7 col3\" >2.28%</td>\n",
       "      <td id=\"T_c2e95_row7_col4\" class=\"data row7 col4\" >0.22%</td>\n",
       "      <td id=\"T_c2e95_row7_col5\" class=\"data row7 col5\" >2.26%</td>\n",
       "      <td id=\"T_c2e95_row7_col6\" class=\"data row7 col6\" >10.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row8\" class=\"row_heading level0 row8\" >20.660000</th>\n",
       "      <td id=\"T_c2e95_row8_col0\" class=\"data row8 col0\" >-2.31%</td>\n",
       "      <td id=\"T_c2e95_row8_col1\" class=\"data row8 col1\" >-1.52%</td>\n",
       "      <td id=\"T_c2e95_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row8_col3\" class=\"data row8 col3\" >-1.19%</td>\n",
       "      <td id=\"T_c2e95_row8_col4\" class=\"data row8 col4\" >3.65%</td>\n",
       "      <td id=\"T_c2e95_row8_col5\" class=\"data row8 col5\" >2.60%</td>\n",
       "      <td id=\"T_c2e95_row8_col6\" class=\"data row8 col6\" >13.44%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row9\" class=\"row_heading level0 row9\" >28.930000</th>\n",
       "      <td id=\"T_c2e95_row9_col0\" class=\"data row9 col0\" >-1.90%</td>\n",
       "      <td id=\"T_c2e95_row9_col1\" class=\"data row9 col1\" >0.86%</td>\n",
       "      <td id=\"T_c2e95_row9_col2\" class=\"data row9 col2\" >2.55%</td>\n",
       "      <td id=\"T_c2e95_row9_col3\" class=\"data row9 col3\" >3.04%</td>\n",
       "      <td id=\"T_c2e95_row9_col4\" class=\"data row9 col4\" >0.12%</td>\n",
       "      <td id=\"T_c2e95_row9_col5\" class=\"data row9 col5\" >3.40%</td>\n",
       "      <td id=\"T_c2e95_row9_col6\" class=\"data row9 col6\" >6.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row10\" class=\"row_heading level0 row10\" >40.500000</th>\n",
       "      <td id=\"T_c2e95_row10_col0\" class=\"data row10 col0\" >-3.01%</td>\n",
       "      <td id=\"T_c2e95_row10_col1\" class=\"data row10 col1\" >-0.20%</td>\n",
       "      <td id=\"T_c2e95_row10_col2\" class=\"data row10 col2\" >-0.69%</td>\n",
       "      <td id=\"T_c2e95_row10_col3\" class=\"data row10 col3\" >5.52%</td>\n",
       "      <td id=\"T_c2e95_row10_col4\" class=\"data row10 col4\" >1.65%</td>\n",
       "      <td id=\"T_c2e95_row10_col5\" class=\"data row10 col5\" >0.95%</td>\n",
       "      <td id=\"T_c2e95_row10_col6\" class=\"data row10 col6\" >12.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row11\" class=\"row_heading level0 row11\" >56.690000</th>\n",
       "      <td id=\"T_c2e95_row11_col0\" class=\"data row11 col0\" >-1.35%</td>\n",
       "      <td id=\"T_c2e95_row11_col1\" class=\"data row11 col1\" >-0.52%</td>\n",
       "      <td id=\"T_c2e95_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row11_col3\" class=\"data row11 col3\" >6.09%</td>\n",
       "      <td id=\"T_c2e95_row11_col4\" class=\"data row11 col4\" >0.67%</td>\n",
       "      <td id=\"T_c2e95_row11_col5\" class=\"data row11 col5\" >-2.74%</td>\n",
       "      <td id=\"T_c2e95_row11_col6\" class=\"data row11 col6\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row12\" class=\"row_heading level0 row12\" >79.370000</th>\n",
       "      <td id=\"T_c2e95_row12_col0\" class=\"data row12 col0\" >-2.28%</td>\n",
       "      <td id=\"T_c2e95_row12_col1\" class=\"data row12 col1\" >-2.92%</td>\n",
       "      <td id=\"T_c2e95_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row12_col3\" class=\"data row12 col3\" >5.80%</td>\n",
       "      <td id=\"T_c2e95_row12_col4\" class=\"data row12 col4\" >-0.33%</td>\n",
       "      <td id=\"T_c2e95_row12_col5\" class=\"data row12 col5\" ></td>\n",
       "      <td id=\"T_c2e95_row12_col6\" class=\"data row12 col6\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row13\" class=\"row_heading level0 row13\" >111.120000</th>\n",
       "      <td id=\"T_c2e95_row13_col0\" class=\"data row13 col0\" >-3.55%</td>\n",
       "      <td id=\"T_c2e95_row13_col1\" class=\"data row13 col1\" >-5.13%</td>\n",
       "      <td id=\"T_c2e95_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row13_col3\" class=\"data row13 col3\" ></td>\n",
       "      <td id=\"T_c2e95_row13_col4\" class=\"data row13 col4\" ></td>\n",
       "      <td id=\"T_c2e95_row13_col5\" class=\"data row13 col5\" ></td>\n",
       "      <td id=\"T_c2e95_row13_col6\" class=\"data row13 col6\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c2e95_level0_row14\" class=\"row_heading level0 row14\" >155.570000</th>\n",
       "      <td id=\"T_c2e95_row14_col0\" class=\"data row14 col0\" ></td>\n",
       "      <td id=\"T_c2e95_row14_col1\" class=\"data row14 col1\" >-3.80%</td>\n",
       "      <td id=\"T_c2e95_row14_col2\" class=\"data row14 col2\" ></td>\n",
       "      <td id=\"T_c2e95_row14_col3\" class=\"data row14 col3\" ></td>\n",
       "      <td id=\"T_c2e95_row14_col4\" class=\"data row14 col4\" ></td>\n",
       "      <td id=\"T_c2e95_row14_col5\" class=\"data row14 col5\" ></td>\n",
       "      <td id=\"T_c2e95_row14_col6\" class=\"data row14 col6\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cc23ea30>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.03941080781534606\n",
      "R-squared: -6.7106\n",
      "RMSE: 0.1512\n",
      "[0.68809273 0.25233889]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0108\n",
      "Universal Metric of SM2: 0.0836\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()\n",
    "\n",
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
